Explore the CAZome of Pectobacteriaceae genomes

This notebook explores the size and composition of 717 Genbank Pectobacteriaceae CAZomes.

GitHub

Information of the complete method for this analysis, including augmenting the dataset, a README-walkthrough, and the output figure files, can be found in the GitHub repository.

Table of Contents

1. Imports
   • Load packages
   • Load in data
2. CAZome size
   • Compare the number of CAZymes
   • Compare the proportion of the proteome represented by the CAZomes
3. CAZy classes
   • The number of CAZymes per CAZy class
   • Mean (+/- SD) number of CAZymes per CAZy class per genus
4. CAZy families
   • Calculate CAZy family frequencies per genome
   • Plot a clustermap of CAZy family frequencies
5. Core CAZome
   • Identify families that are present in all genomes
   • Calculate the frequency of families in the core CAZome
   • Pectobacterium core CAZome
   • Dickeya
6. Always co-occurring families
   • Identify CAZy families that are always present in the genome together
   • Explore across all Pectobacterium and Dickeya genomes, and per genus
   • Build an upset plot of co-occurring CAZy families
   • Compile a matrix with the indcidence data for each group of co-occurring CAZy families
7. Principal Component Analysis (PCA)
   • Explore the association between the host range, global distribution and composition of the CAZome
   • Explore across all Pectobacterium and Dickeya genomes, and per genus

0. Imports

Packages

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statistics
import re
import time

from copy import copy
from matplotlib.patches import Patch
from pathlib import Path

import adjustText
import upsetplot

from Bio import SeqIO
from saintBioutils.utilities.file_io.get_paths import get_file_paths
from saintBioutils.utilities.file_io import make_output_directory
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from tqdm.notebook import tqdm

%matplotlib inline

# loading and parsing data
from cazomevolve.cazome.explore.parse_data import (
    load_fgp_data,
    load_tax_data,
    add_tax_data_from_tax_df,
    add_tax_column_from_row_index,
)

# functions for exploring the sizes of CAZomes
from cazomevolve.cazome.explore.cazome_sizes import (
    calc_proteome_representation,
    count_items_in_cazome,
    get_proteome_sizes,
    count_cazyme_fam_ratio,
)

# explore the frequency of CAZymes per CAZy class
from cazomevolve.cazome.explore.cazy_classes import calculate_class_sizes

# explore the frequencies of CAZy families and identify the co-cazome
from cazomevolve.cazome.explore.cazy_families import (
    build_fam_freq_df,
    build_row_colours,
    build_family_clustermap,
    identify_core_cazome,
    plot_fam_boxplot,
    build_fam_mean_freq_df,
    get_group_specific_fams,
    build_family_clustermap_multi_legend,
)

# functions to identify and explore CAZy families that are always present together
from cazomevolve.cazome.explore.cooccurring_families import (
    identify_cooccurring_fams_corrM,
    calc_cooccuring_fam_freqs,
    identify_cooccurring_fam_pairs,
    add_to_upsetplot_membership,
    build_upsetplot,
    get_upsetplot_grps,
    add_upsetplot_grp_freqs,
    build_upsetplot_matrix,
)

# functions to perform PCA
from cazomevolve.cazome.explore.pca import (
    perform_pca,
    plot_explained_variance,
    plot_scree,
    plot_pca,
    plot_loadings,
)

Output directory

To make the parent output directory, and further down to make output subdirectories, use the function make_output_directory from the saintBioutils package.

One positional argument is required: the path to the target output directory to be build - this must be a Path object.

The path for the output directory must be provided as a **Path** object from the `pathlib` package.

Setting force and nodelete to True will ensure the output directory is created, and if it exists, content in the output directory is not deleted.

# make output dir for results, will not delete data if dir already exists
make_output_directory(Path('../results'), force=True, nodelete=True)

Built output directory: ../results

Import Data

CAZy family annotations

To import CAZy family annotations, load the tab delimited file listing cazy families, genomes and protein accessions, by providing the path to the 'gfp file' to load_gfp_data() function from cazomevolve.

Note: Each unique protein-family pair is represented on a separate line. A protein can contain multiple CAZyme domains and thus can be annotated with multiple CAZy families. Therefore, a single protein can be present on multiple rows in the dataframe called gfp_df.

fgp_file = "../data/cazomes/pecto_fam_genomes_proteins"
fgp_df = load_fgp_data(fgp_file)
fgp_df.head(3)

	Family	Genome	Protein
0	CBM50	GCA_003382565.3	UEM40323.1
1	GT35	GCA_003382565.3	UEM39157.1
2	GH5	GCA_003382565.3	UEM41238.1

print(f"Total CAZymes (i.e. the number of unique protein IDs): {len(set(fgp_df['Protein']))}")

Total CAZymes (i.e. the number of unique protein IDs): 78132

Taxonomy data

Load in CSV containing taxonomic information, that was generated using the cazomevolve subcommand add_taxs, by providing a path to the file to load_tax_data() function from cazomevovle, and specify which taxonomic ranks (kingdom, phylum, etc.) are listed in the CSV file.

In this case, genus and species inforamtion was retrieved from the NCBI database and are stored in the CSV file.

Note: The CSV file created using the cazomevolve subcommand "add_taxs" will contain a column called 'Genome', listing genomic accessions, and one column per taxonomic rank retrieved from NCBI and/or GTDB.

tax_csv_path = "../data/cazomes/fg_genome_taxs.csv"
tax_df = load_tax_data(tax_csv_path, genus=True, species=True)
tax_df.head(3)

	Genome	Genus	Species
0	GCA_922021645.1	Pectobacterium	versatile
1	GCA_004296685.1	Pectobacterium	versatile
2	GCA_018094705.1	Pectobacterium	versatile

Compile all data into a single dataframe

Build dataframe of:

• CAZy family annotations
• Genomic accession
• Taxonomic information - splitting each taxonomy rank (i.e. ranks) into a separate column. E.g.:
  • Genus
  • Species

fgp_df = add_tax_data_from_tax_df(
    fgp_df,
    tax_df,
    genus=True,
    species=True,
)
fgp_df.head(3)

Collecting Genus data: 100%|██████████| 83143/83143 [00:59<00:00, 1392.08it/s]
Collecting Species data: 100%|██████████| 83143/83143 [01:01<00:00, 1350.43it/s]

	Family	Genome	Protein	Genus	Species
0	CBM50	GCA_003382565.3	UEM40323.1	Pectobacterium	aquaticum
1	GT35	GCA_003382565.3	UEM39157.1	Pectobacterium	aquaticum
2	GH5	GCA_003382565.3	UEM41238.1	Pectobacterium	aquaticum

print(f"Total CAZymes: {len(set(fgp_df['Protein']))}")

Total CAZymes: 78132

1. CAZome size

Explore and compare the sizes of the CAZomes, by calculating:

• The number of CAZymes per genome
• The mean number of CAZymes per genome per genus
• The proportion of the proteome represented by the CAZome
• The mean proportion of the proteome represented by the CAZome

Note: The number of CAZymes is defined as the unique number of protein IDs.

Use the count_items_in_cazome() function to retrieve the number of CAZymes and the number of CAZy families per genome, and the mean (and standard deviation (SD)) counts per genus.

# check all genomes are represented in the fgp_df
f"Examining {len(set(fgp_df['Genome']))} genomes"

'Examining 717 genomes'

print(f"Examining {len(set(fgp_df['Genus']))} genera:")
for genus in set(fgp_df['Genus']):
    print(f'- {genus}')

Examining 8 genera:
- Dickeya
- Pectobacterium
- Brenneria
- Affinibrenneria
- Samsonia
- Lonsdalea
- Acerihabitans
- Musicola

Count the number of CAZymes

# Calculate CAZymes per genome
cazome_sizes_dict, cazome_sizes_df = count_items_in_cazome(fgp_df, 'Protein', 'Genus', round_by=2)
cazome_sizes_df

Gathering CAZy families per genome: 100%|██████████| 83143/83143 [00:10<00:00, 8297.22it/s]
Calculating num of Protein per genome and per Genus: 100%|██████████| 8/8 [00:00<00:00, 2959.73it/s]

	Genus	MeanProteins	SdProteins	NumOfGenomes
0	Pectobacterium	112.65	8.02	432
1	Dickeya	111.16	6.60	206
2	Musicola	92.25	2.28	4
3	Brenneria	87.79	7.46	33
4	Lonsdalea	77.15	4.70	39
5	Acerihabitans	106.00	0.00	1
6	Affinibrenneria	108.00	0.00	1
7	Samsonia	81.00	0.00	1

# calculate mean across pectobacteriaceae
pectobact_cazome_sizes = []
for genus in cazome_sizes_dict:
    for genome in cazome_sizes_dict[genus]:
        pectobact_cazome_sizes.append(cazome_sizes_dict[genus][genome]['numOfProteins'])

pd.concat(
    [
        cazome_sizes_df, 
        pd.DataFrame(
            [[
                'Pectobacteriaceae',
                np.mean(pectobact_cazome_sizes),
                np.std(pectobact_cazome_sizes),
                len(set(fgp_df['Genome'])),
            ]], 
            columns=cazome_sizes_df.columns
        ),
    ],
    axis=0,
)

	Genus	MeanProteins	SdProteins	NumOfGenomes
0	Pectobacterium	112.650000	8.020000	432
1	Dickeya	111.160000	6.600000	206
2	Musicola	92.250000	2.280000	4
3	Brenneria	87.790000	7.460000	33
4	Lonsdalea	77.150000	4.700000	39
5	Acerihabitans	106.000000	0.000000	1
6	Affinibrenneria	108.000000	0.000000	1
7	Samsonia	81.000000	0.000000	1
0	Pectobacteriaceae	108.970711	11.958225	717

Identify the total number of CAZymes

print(f"The total number of CAZymes is {len(set(fgp_df['Protein']))}")

for genus in set(fgp_df['Genus']):
    genus_df = fgp_df[fgp_df['Genus'] == genus]
    print(f"The total number of {genus} CAZymes is {len(set(genus_df['Protein']))}")

The total number of CAZymes is 78132
The total number of Dickeya CAZymes is 22899
The total number of Pectobacterium CAZymes is 48663
The total number of Brenneria CAZymes is 2897
The total number of Affinibrenneria CAZymes is 108
The total number of Samsonia CAZymes is 81
The total number of Lonsdalea CAZymes is 3009
The total number of Acerihabitans CAZymes is 106
The total number of Musicola CAZymes is 369

Count the number of CAZy families

# Calculate CAZy families per genome
cazome_fam_dict, cazome_fams_df = count_items_in_cazome(fgp_df, 'Family', 'Genus', round_by=2)
cazome_fams_df

Gathering CAZy families per genome: 100%|██████████| 83143/83143 [00:10<00:00, 8279.61it/s]
Calculating num of Family per genome and per Genus: 100%|██████████| 8/8 [00:00<00:00, 3237.28it/s]

	Genus	MeanFamilys	SdFamilys	NumOfGenomes
0	Pectobacterium	62.08	3.46	432
1	Dickeya	59.05	3.04	206
2	Musicola	50.25	0.43	4
3	Brenneria	53.67	3.71	33
4	Lonsdalea	42.62	2.14	39
5	Acerihabitans	48.00	0.00	1
6	Affinibrenneria	48.00	0.00	1
7	Samsonia	49.00	0.00	1

# calculate mean across pectobacteriaceae
pectobact_fam_nums = []
for genus in cazome_fam_dict:
    for genome in cazome_fam_dict[genus]:
        pectobact_fam_nums.append(cazome_fam_dict[genus][genome]['numOfFamilys'])

pd.concat(
    [
        cazome_fams_df, 
        pd.DataFrame(
            [[
                'Pectobacteriaceae',
                np.mean(pectobact_fam_nums),
                np.std(pectobact_fam_nums),
                len(set(fgp_df['Genome'])),
            ]], 
            columns=cazome_fams_df.columns
        ),
    ],
    axis=0,
)

	Genus	MeanFamilys	SdFamilys	NumOfGenomes
0	Pectobacterium	62.080000	3.460000	432
1	Dickeya	59.050000	3.040000	206
2	Musicola	50.250000	0.430000	4
3	Brenneria	53.670000	3.710000	33
4	Lonsdalea	42.620000	2.140000	39
5	Acerihabitans	48.000000	0.000000	1
6	Affinibrenneria	48.000000	0.000000	1
7	Samsonia	49.000000	0.000000	1
0	Pectobacteriaceae	59.638773	5.733681	717

Calculate ratio of CAZymes to CAZy families

The CAZyme to CAZy families ratio can be used to determine whether a genus typically contains few or many CAZymes per CAZy family.

cazome_ratio_dict, cazome_ratio_df = count_cazyme_fam_ratio(fgp_df, 'Genus', round_by=2)
cazome_ratio_df

Gathering CAZymes and CAZy families per genome: 100%|██████████| 83143/83143 [00:10<00:00, 8042.17it/s]
Calculating CAZyme/CAZy family ratio: 100%|██████████| 8/8 [00:00<00:00, 3315.33it/s]

	Genus	MeanCAZymeToFamRatio	SdCAZymeToFamRatio	NumOfGenomes
0	Pectobacterium	1.81	0.09	432
1	Dickeya	1.88	0.07	206
2	Musicola	1.84	0.04	4
3	Brenneria	1.64	0.07	33
4	Lonsdalea	1.81	0.07	39
5	Acerihabitans	2.21	0.00	1
6	Affinibrenneria	2.25	0.00	1
7	Samsonia	1.65	0.00	1

pecto_ratios = []
for genus in cazome_sizes_dict:
    for genome in cazome_sizes_dict[genus]:
        ratio = (
            cazome_sizes_dict[genus][genome]['numOfProteins'] / cazome_fam_dict[genus][genome]['numOfFamilys']
        )
        pecto_ratios.append(ratio)

pd.concat(
    [
        cazome_ratio_df,
        pd.DataFrame(
            [[
                'Pectobacteriaceae',
                np.mean(pecto_ratios),
                np.std(pecto_ratios),
                len(set(fgp_df['Genome'])),
            ]],
            columns=cazome_ratio_df.columns
        )
    ],
    axis=0,
)

	Genus	MeanCAZymeToFamRatio	SdCAZymeToFamRatio	NumOfGenomes
0	Pectobacterium	1.810000	0.09000	432
1	Dickeya	1.880000	0.07000	206
2	Musicola	1.840000	0.04000	4
3	Brenneria	1.640000	0.07000	33
4	Lonsdalea	1.810000	0.07000	39
5	Acerihabitans	2.210000	0.00000	1
6	Affinibrenneria	2.250000	0.00000	1
7	Samsonia	1.650000	0.00000	1
0	Pectobacteriaceae	1.826642	0.09796	717

Calculate proteome sizes

Calculate the number of unique protein IDs, total, in each genome - i.e. not just CAZymes but all proteins.

# Get the size of the proteome (the number of protein acc) per genome
grp = 'Genus'
proteome_dir = "../data/proteomes"
proteome_dict = get_proteome_sizes(proteome_dir, fgp_df, grp)

Getting proteome sizes: 0it [00:00, ?it/s]

# get total number of proteins across all proteomes
total_proteins = 0
for genus in proteome_dict:
    for genome in proteome_dict[genus]:
        total_proteins += proteome_dict[genus][genome]['numOfProteins']
print(f"Total number of proteins across all genomes: {total_proteins}")

Total number of proteins across all genomes: 0

Calculate the percentage of the proteome represented by the CAZome

Calculate the percentage of the proteome that is made up of the CAZome.

# Calculate the mean proteome size by genus and the proportion of the proteome represented by the CAZome
proteome_perc_df = calc_proteome_representation(proteome_dict, cazome_sizes_dict, grp, round_by=2)
proteome_perc_df

Getting proteome size: 100%|██████████| 8/8 [00:00<00:00, 57.97it/s]

Not proteome size available for GCA_003382565.3.
Skipping this genome
Not proteome size available for GCA_013488025.1.
Skipping this genome
Not proteome size available for GCA_024722495.1.
Skipping this genome
Not proteome size available for GCA_024498855.1.
Skipping this genome
Not proteome size available for GCA_012427845.1.
Skipping this genome
Not proteome size available for GCA_016944275.1.
Skipping this genome
Not proteome size available for GCA_002307355.1.
Skipping this genome
Not proteome size available for GCA_016944235.1.
Skipping this genome
Not proteome size available for GCA_016864995.1.
Skipping this genome
Not proteome size available for GCA_016944255.1.
Skipping this genome
Not proteome size available for GCA_001742185.1.
Skipping this genome
Not proteome size available for GCA_002288545.1.
Skipping this genome
Not proteome size available for GCA_021049225.1.
Skipping this genome
Not proteome size available for GCA_002764035.1.
Skipping this genome
Not proteome size available for GCA_020865565.1.
Skipping this genome
Not proteome size available for GCA_022477975.1.
Skipping this genome
Not proteome size available for GCA_016864935.1.
Skipping this genome
Not proteome size available for GCA_024498675.1.
Skipping this genome
Not proteome size available for GCA_000696465.1.
Skipping this genome
Not proteome size available for GCA_020047135.1.
Skipping this genome
Not proteome size available for GCA_003628615.1.
Skipping this genome
Not proteome size available for GCA_009931215.1.
Skipping this genome
Not proteome size available for GCA_024498815.1.
Skipping this genome
Not proteome size available for GCA_024498775.1.
Skipping this genome
Not proteome size available for GCA_009873295.1.
Skipping this genome
Not proteome size available for GCA_018634035.1.
Skipping this genome
Not proteome size available for GCA_003628635.1.
Skipping this genome
Not proteome size available for GCA_009931575.1.
Skipping this genome
Not proteome size available for GCA_019056595.1.
Skipping this genome
Not proteome size available for GCA_003932035.1.
Skipping this genome
Not proteome size available for GCA_020971565.1.
Skipping this genome
Not proteome size available for GCA_003628735.1.
Skipping this genome
Not proteome size available for GCA_020423065.1.
Skipping this genome
Not proteome size available for GCA_016415585.1.
Skipping this genome
Not proteome size available for GCA_020423085.1.
Skipping this genome
Not proteome size available for GCA_002921235.2.
Skipping this genome
Not proteome size available for GCA_003031305.1.
Skipping this genome
Not proteome size available for GCA_016944615.1.
Skipping this genome
Not proteome size available for GCA_022392805.1.
Skipping this genome
Not proteome size available for GCA_002068115.1.
Skipping this genome
Not proteome size available for GCA_018141505.2.
Skipping this genome
Not proteome size available for GCA_025643735.1.
Skipping this genome
Not proteome size available for GCA_003990515.2.
Skipping this genome
Not proteome size available for GCA_002920045.2.
Skipping this genome
Not proteome size available for GCA_016864975.1.
Skipping this genome
Not proteome size available for GCA_000023605.1.
Skipping this genome
Not proteome size available for GCA_024498735.1.
Skipping this genome
Not proteome size available for GCA_009931295.1.
Skipping this genome
Not proteome size available for GCA_025946765.1.
Skipping this genome
Not proteome size available for GCA_001742145.1.
Skipping this genome
Not proteome size available for GCA_016944315.1.
Skipping this genome
Not proteome size available for GCA_003628715.1.
Skipping this genome
Not proteome size available for GCA_014946825.2.
Skipping this genome
Not proteome size available for GCA_003628695.1.
Skipping this genome
Not proteome size available for GCA_003628575.1.
Skipping this genome
Not proteome size available for GCA_024498795.1.
Skipping this genome
Not proteome size available for GCA_000769535.1.
Skipping this genome
Not proteome size available for GCA_022392325.1.
Skipping this genome
Not proteome size available for GCA_016495705.1.
Skipping this genome
Not proteome size available for GCA_015689195.1.
Skipping this genome
Not proteome size available for GCA_024498755.1.
Skipping this genome
Not proteome size available for GCA_014946775.2.
Skipping this genome
Not proteome size available for GCA_009931535.1.
Skipping this genome
Not proteome size available for GCA_009931555.1.
Skipping this genome
Not proteome size available for GCA_016944555.1.
Skipping this genome
Not proteome size available for GCA_024498695.1.
Skipping this genome
Not proteome size available for GCA_020683105.1.
Skipping this genome
Not proteome size available for GCA_000294535.1.
Skipping this genome
Not proteome size available for GCA_000740965.1.
Skipping this genome
Not proteome size available for GCA_023272715.1.
Skipping this genome
Not proteome size available for GCA_021907015.1.
Skipping this genome
Not proteome size available for GCA_003992745.1.
Skipping this genome
Not proteome size available for GCA_024498875.1.
Skipping this genome
Not proteome size available for GCA_003628675.1.
Skipping this genome
Not proteome size available for GCA_016126895.1.
Skipping this genome
Not proteome size available for GCA_024758625.1.
Skipping this genome
Not proteome size available for GCA_028335745.1.
Skipping this genome
Not proteome size available for GCA_001932635.1.
Skipping this genome
Not proteome size available for GCA_009931195.1.
Skipping this genome
Not proteome size available for GCA_016944595.1.
Skipping this genome
Not proteome size available for GCA_016944295.1.
Skipping this genome
Not proteome size available for GCA_016944435.1.
Skipping this genome
Not proteome size available for GCA_000024645.1.
Skipping this genome
Not proteome size available for GCA_003571485.1.
Skipping this genome
Not proteome size available for GCA_020423105.1.
Skipping this genome
Not proteome size available for GCA_000011605.1.
Skipping this genome
Not proteome size available for GCA_003628655.1.
Skipping this genome
Not proteome size available for GCA_025402815.1.
Skipping this genome
Not proteome size available for GCA_003628595.1.
Skipping this genome
Not proteome size available for GCA_020520265.1.
Skipping this genome
Not proteome size available for GCA_002804265.1.
Skipping this genome
Not proteome size available for GCA_024498835.1.
Skipping this genome
Not proteome size available for GCA_024498715.1.
Skipping this genome
Not proteome size available for GCA_022220705.1.
Skipping this genome
Not proteome size available for GCA_007833835.1.
Skipping this genome
Not proteome size available for GCA_000260925.1.
Skipping this genome
Not proteome size available for GCA_946480005.1.
Skipping this genome
Not proteome size available for GCA_922030515.1.
Skipping this genome
Not proteome size available for GCA_002904055.1.
Skipping this genome
Not proteome size available for GCA_013449605.1.
Skipping this genome
Not proteome size available for GCA_025466295.1.
Skipping this genome
Not proteome size available for GCA_003628025.1.
Skipping this genome
Not proteome size available for GCA_000808335.1.
Skipping this genome
Not proteome size available for GCA_004296715.1.
Skipping this genome
Not proteome size available for GCA_003121785.1.
Skipping this genome
Not proteome size available for GCA_000749965.1.
Skipping this genome
Not proteome size available for GCA_016949955.1.
Skipping this genome
Not proteome size available for GCA_003382585.2.
Skipping this genome
Not proteome size available for GCA_029075705.1.
Skipping this genome
Not proteome size available for GCA_900195295.2.
Skipping this genome
Not proteome size available for GCA_000749855.1.
Skipping this genome
Not proteome size available for GCA_016742935.1.
Skipping this genome
Not proteome size available for GCA_016950195.1.
Skipping this genome
Not proteome size available for GCA_015277635.1.
Skipping this genome
Not proteome size available for GCA_016107195.1.
Skipping this genome
Not proteome size available for GCA_000808415.1.
Skipping this genome
Not proteome size available for GCA_024343335.1.
Skipping this genome
Not proteome size available for GCA_001429565.2.
Skipping this genome
Not proteome size available for GCA_000808235.1.
Skipping this genome
Not proteome size available for GCA_016950155.1.
Skipping this genome
Not proteome size available for GCA_016107065.1.
Skipping this genome
Not proteome size available for GCA_019426305.1.
Skipping this genome
Not proteome size available for GCA_024342785.1.
Skipping this genome
Not proteome size available for GCA_000389755.1.
Skipping this genome
Not proteome size available for GCA_003382595.2.
Skipping this genome
Not proteome size available for GCA_016949195.1.
Skipping this genome
Not proteome size available for GCA_016642095.1.
Skipping this genome
Not proteome size available for GCA_020406615.1.
Skipping this genome
Not proteome size available for GCA_014946845.1.
Skipping this genome
Not proteome size available for GCA_024343625.1.
Skipping this genome
Not proteome size available for GCA_016949435.1.
Skipping this genome
Not proteome size available for GCA_004296725.1.
Skipping this genome
Not proteome size available for GCA_014042225.1.
Skipping this genome
Not proteome size available for GCA_024343685.1.
Skipping this genome
Not proteome size available for GCA_000754715.1.
Skipping this genome
Not proteome size available for GCA_022507255.1.
Skipping this genome
Not proteome size available for GCA_016949215.1.
Skipping this genome
Not proteome size available for GCA_922022125.1.
Skipping this genome
Not proteome size available for GCA_000808455.1.
Skipping this genome
Not proteome size available for GCA_902143365.1.
Skipping this genome
Not proteome size available for GCA_029524195.1.
Skipping this genome
Not proteome size available for GCA_013449535.1.
Skipping this genome
Not proteome size available for GCA_020181655.1.
Skipping this genome
Not proteome size available for GCA_029524255.1.
Skipping this genome
Not proteome size available for GCA_020406695.1.
Skipping this genome
Not proteome size available for GCA_016107025.1.
Skipping this genome
Not proteome size available for GCA_024343035.1.
Skipping this genome
Not proteome size available for GCA_013449375.1.
Skipping this genome
Not proteome size available for GCA_922037575.1.
Skipping this genome
Not proteome size available for GCA_024343295.1.
Skipping this genome
Not proteome size available for GCA_020296625.1.
Skipping this genome
Not proteome size available for GCA_020406785.1.
Skipping this genome
Not proteome size available for GCA_922011605.1.
Skipping this genome
Not proteome size available for GCA_013449405.1.
Skipping this genome
Not proteome size available for GCA_003382625.2.
Skipping this genome
Not proteome size available for GCA_029027905.1.
Skipping this genome
Not proteome size available for GCA_024343355.1.
Skipping this genome
Not proteome size available for GCA_000808435.1.
Skipping this genome
Not proteome size available for GCA_003595135.1.
Skipping this genome
Not proteome size available for GCA_922019785.1.
Skipping this genome
Not proteome size available for GCA_020297385.1.
Skipping this genome
Not proteome size available for GCA_018095745.1.
Skipping this genome
Not proteome size available for GCA_014604455.1.
Skipping this genome
Not proteome size available for GCA_020166455.1.
Skipping this genome
Not proteome size available for GCA_022507345.1.
Skipping this genome
Not proteome size available for GCA_002904085.1.
Skipping this genome
Not proteome size available for GCA_016949525.1.
Skipping this genome
Not proteome size available for GCA_003594975.1.
Skipping this genome
Not proteome size available for GCA_902143355.1.
Skipping this genome
Not proteome size available for GCA_013449395.1.
Skipping this genome
Not proteome size available for GCA_003595115.1.
Skipping this genome
Not proteome size available for GCA_018344415.1.
Skipping this genome
Not proteome size available for GCA_020406595.1.
Skipping this genome
Not proteome size available for GCA_003990505.1.
Skipping this genome
Not proteome size available for GCA_003595035.1.
Skipping this genome
Not proteome size available for GCA_922011735.1.
Skipping this genome
Not proteome size available for GCA_000632375.1.
Skipping this genome
Not proteome size available for GCA_000754685.1.
Skipping this genome
Not proteome size available for GCA_016107085.1.
Skipping this genome
Not proteome size available for GCA_013449715.1.
Skipping this genome
Not proteome size available for GCA_020406955.1.
Skipping this genome
Not proteome size available for GCA_000291725.1.
Skipping this genome
Not proteome size available for GCA_922021645.1.
Skipping this genome
Not proteome size available for GCA_922013345.1.
Skipping this genome
Not proteome size available for GCA_003595125.1.
Skipping this genome
Not proteome size available for GCA_002904005.1.
Skipping this genome
Not proteome size available for GCA_003028395.1.
Skipping this genome
Not proteome size available for GCA_024343655.1.
Skipping this genome
Not proteome size available for GCA_014946865.1.
Skipping this genome
Not proteome size available for GCA_016950085.1.
Skipping this genome
Not proteome size available for GCA_922035905.1.
Skipping this genome
Not proteome size available for GCA_014050115.1.
Skipping this genome
Not proteome size available for GCA_016949545.1.
Skipping this genome
Not proteome size available for GCA_028736315.1.
Skipping this genome
Not proteome size available for GCA_922015415.1.
Skipping this genome
Not proteome size available for GCA_020295565.1.
Skipping this genome
Not proteome size available for GCA_016949085.1.
Skipping this genome
Not proteome size available for GCA_013449685.1.
Skipping this genome
Not proteome size available for GCA_016107035.1.
Skipping this genome
Not proteome size available for GCA_000738115.1.
Skipping this genome
Not proteome size available for GCA_020406855.1.
Skipping this genome
Not proteome size available for GCA_003382655.2.
Skipping this genome
Not proteome size available for GCA_021432585.1.
Skipping this genome
Not proteome size available for GCA_025466305.1.
Skipping this genome
Not proteome size available for GCA_028613535.1.
Skipping this genome
Not proteome size available for GCA_020406755.1.
Skipping this genome
Not proteome size available for GCA_016949835.1.
Skipping this genome
Not proteome size available for GCA_902143345.1.
Skipping this genome
Not proteome size available for GCA_002930555.1.
Skipping this genome
Not proteome size available for GCA_026800125.1.
Skipping this genome
Not proteome size available for GCA_922011625.1.
Skipping this genome
Not proteome size available for GCA_005497185.1.
Skipping this genome
Not proteome size available for GCA_002250255.1.
Skipping this genome
Not proteome size available for GCA_000808195.1.
Skipping this genome
Not proteome size available for GCA_015709085.1.
Skipping this genome
Not proteome size available for GCA_002904145.1.
Skipping this genome
Not proteome size available for GCA_922021695.1.
Skipping this genome
Not proteome size available for GCA_000754705.1.
Skipping this genome
Not proteome size available for GCA_922016765.1.
Skipping this genome
Not proteome size available for GCA_000803315.1.
Skipping this genome
Not proteome size available for GCA_013449385.1.
Skipping this genome
Not proteome size available for GCA_011378985.1.
Skipping this genome
Not proteome size available for GCA_922022605.1.
Skipping this genome
Not proteome size available for GCA_013913595.1.
Skipping this genome
Not proteome size available for GCA_000754695.1.
Skipping this genome
Not proteome size available for GCA_016950255.1.
Skipping this genome
Not proteome size available for GCA_001038685.1.
Skipping this genome
Not proteome size available for GCA_002904185.1.
Skipping this genome
Not proteome size available for GCA_922013315.1.
Skipping this genome
Not proteome size available for GCA_004137785.1.
Skipping this genome
Not proteome size available for GCA_002904195.1.
Skipping this genome
Not proteome size available for GCA_016949775.1.
Skipping this genome
Not proteome size available for GCA_016949235.1.
Skipping this genome
Not proteome size available for GCA_016949055.1.
Skipping this genome
Not proteome size available for GCA_000749905.1.
Skipping this genome
Not proteome size available for GCA_020406655.1.
Skipping this genome
Not proteome size available for GCA_025809395.1.
Skipping this genome
Not proteome size available for GCA_014164815.1.
Skipping this genome
Not proteome size available for GCA_002904045.1.
Skipping this genome
Not proteome size available for GCA_016949935.1.
Skipping this genome
Not proteome size available for GCA_000173155.1.
Skipping this genome
Not proteome size available for GCA_002920025.1.
Skipping this genome
Not proteome size available for GCA_000757625.1.
Skipping this genome
Not proteome size available for GCA_013449655.1.
Skipping this genome
Not proteome size available for GCA_002921275.1.
Skipping this genome
Not proteome size available for GCA_020406835.1.
Skipping this genome
Not proteome size available for GCA_024343135.1.
Skipping this genome
Not proteome size available for GCA_016742895.1.
Skipping this genome
Not proteome size available for GCA_016949735.1.
Skipping this genome
Not proteome size available for GCA_000808355.1.
Skipping this genome
Not proteome size available for GCA_016107095.1.
Skipping this genome
Not proteome size available for GCA_013913645.1.
Skipping this genome
Not proteome size available for GCA_024343445.1.
Skipping this genome
Not proteome size available for GCA_016949145.1.
Skipping this genome
Not proteome size available for GCA_022507325.1.
Skipping this genome
Not proteome size available for GCA_021375765.1.
Skipping this genome
Not proteome size available for GCA_922011595.1.
Skipping this genome
Not proteome size available for GCA_020295605.1.
Skipping this genome
Not proteome size available for GCA_029075715.1.
Skipping this genome
Not proteome size available for GCA_900129615.1.
Skipping this genome
Not proteome size available for GCA_020295425.1.
Skipping this genome
Not proteome size available for GCA_003402555.1.
Skipping this genome
Not proteome size available for GCA_018095725.1.
Skipping this genome
Not proteome size available for GCA_016950075.1.
Skipping this genome
Not proteome size available for GCA_024343495.1.
Skipping this genome
Not proteome size available for GCA_002921205.1.
Skipping this genome
Not proteome size available for GCA_020406735.1.
Skipping this genome
Not proteome size available for GCA_013449495.1.
Skipping this genome
Not proteome size available for GCA_016949355.1.
Skipping this genome
Not proteome size available for GCA_000808515.1.
Skipping this genome
Not proteome size available for GCA_016949315.1.
Skipping this genome
Not proteome size available for GCA_922013215.1.
Skipping this genome
Not proteome size available for GCA_016949655.1.
Skipping this genome
Not proteome size available for GCA_000749865.1.
Skipping this genome
Not proteome size available for GCA_000808295.1.
Skipping this genome
Not proteome size available for GCA_016742905.1.
Skipping this genome
Not proteome size available for GCA_016949995.1.
Skipping this genome
Not proteome size available for GCA_000738125.1.
Skipping this genome
Not proteome size available for GCA_016949035.1.
Skipping this genome
Not proteome size available for GCA_002250215.1.
Skipping this genome
Not proteome size available for GCA_018094665.1.
Skipping this genome
Not proteome size available for GCA_018094745.1.
Skipping this genome
Not proteome size available for GCA_922037565.1.
Skipping this genome
Not proteome size available for GCA_004296665.1.
Skipping this genome
Not proteome size available for GCA_014946905.1.
Skipping this genome
Not proteome size available for GCA_016949885.1.
Skipping this genome
Not proteome size available for GCA_001039055.1.
Skipping this genome
Not proteome size available for GCA_011379045.1.
Skipping this genome
Not proteome size available for GCA_016949855.1.
Skipping this genome
Not proteome size available for GCA_902167255.1.
Skipping this genome
Not proteome size available for GCA_001238575.1.
Skipping this genome
Not proteome size available for GCA_016949795.1.
Skipping this genome
Not proteome size available for GCA_016950015.1.
Skipping this genome
Not proteome size available for GCA_002904225.1.
Skipping this genome
Not proteome size available for GCA_013449675.1.
Skipping this genome
Not proteome size available for GCA_002904025.1.
Skipping this genome
Not proteome size available for GCA_000808495.1.
Skipping this genome
Not proteome size available for GCA_013913555.1.
Skipping this genome
Not proteome size available for GCA_002921285.1.
Skipping this genome
Not proteome size available for GCA_013449415.1.
Skipping this genome
Not proteome size available for GCA_004296675.1.
Skipping this genome
Not proteome size available for GCA_002847345.1.
Skipping this genome
Not proteome size available for GCA_003121725.1.
Skipping this genome
Not proteome size available for GCA_000808145.1.
Skipping this genome
Not proteome size available for GCA_003121765.1.
Skipping this genome
Not proteome size available for GCA_000754765.1.
Skipping this genome
Not proteome size available for GCA_011378945.1.
Skipping this genome
Not proteome size available for GCA_016950125.1.
Skipping this genome
Not proteome size available for GCA_016949515.1.
Skipping this genome
Not proteome size available for GCA_922030705.1.
Skipping this genome
Not proteome size available for GCA_001038675.1.
Skipping this genome
Not proteome size available for GCA_020166465.1.
Skipping this genome
Not proteome size available for GCA_016949075.1.
Skipping this genome
Not proteome size available for GCA_000749915.1.
Skipping this genome
Not proteome size available for GCA_014946805.1.
Skipping this genome
Not proteome size available for GCA_029524165.1.
Skipping this genome
Not proteome size available for GCA_002921295.1.
Skipping this genome
Not proteome size available for GCA_922031085.1.
Skipping this genome
Not proteome size available for GCA_013450045.1.
Skipping this genome
Not proteome size available for GCA_016949375.1.
Skipping this genome
Not proteome size available for GCA_024342865.1.
Skipping this genome
Not proteome size available for GCA_000749925.1.
Skipping this genome
Not proteome size available for GCA_022507225.1.
Skipping this genome
Not proteome size available for GCA_922011655.1.
Skipping this genome
Not proteome size available for GCA_002904105.1.
Skipping this genome
Not proteome size available for GCA_024343095.1.
Skipping this genome
Not proteome size available for GCA_922021985.1.
Skipping this genome
Not proteome size available for GCA_016950185.1.
Skipping this genome
Not proteome size available for GCA_020166435.1.
Skipping this genome
Not proteome size available for GCA_016949295.1.
Skipping this genome
Not proteome size available for GCA_016107185.1.
Skipping this genome
Not proteome size available for GCA_016949275.1.
Skipping this genome
Not proteome size available for GCA_020406875.1.
Skipping this genome
Not proteome size available for GCA_020295505.1.
Skipping this genome
Not proteome size available for GCA_013913605.1.
Skipping this genome
Not proteome size available for GCA_016949135.1.
Skipping this genome
Not proteome size available for GCA_018728555.1.
Skipping this genome
Not proteome size available for GCA_000808385.1.
Skipping this genome
Not proteome size available for GCA_029023745.1.
Skipping this genome
Not proteome size available for GCA_003121735.1.
Skipping this genome
Not proteome size available for GCA_001984535.1.
Skipping this genome
Not proteome size available for GCA_900195285.2.
Skipping this genome
Not proteome size available for GCA_001984525.1.
Skipping this genome
Not proteome size available for GCA_018094685.1.
Skipping this genome
Not proteome size available for GCA_000803215.1.
Skipping this genome
Not proteome size available for GCA_020295645.1.
Skipping this genome
Not proteome size available for GCA_002904125.1.
Skipping this genome
Not proteome size available for GCA_000749845.1.
Skipping this genome
Not proteome size available for GCA_016949255.1.
Skipping this genome
Not proteome size available for GCA_023914135.1.
Skipping this genome
Not proteome size available for GCA_003800545.1.
Skipping this genome
Not proteome size available for GCA_025466255.1.
Skipping this genome
Not proteome size available for GCA_016950315.1.
Skipping this genome
Not proteome size available for GCA_922014665.1.
Skipping this genome
Not proteome size available for GCA_016950175.1.
Skipping this genome
Not proteome size available for GCA_003595055.1.
Skipping this genome
Not proteome size available for GCA_016950285.1.
Skipping this genome
Not proteome size available for GCA_003186045.1.
Skipping this genome
Not proteome size available for GCA_000808475.1.
Skipping this genome
Not proteome size available for GCA_013449475.1.
Skipping this genome
Not proteome size available for GCA_018219365.1.
Skipping this genome
Not proteome size available for GCA_011378965.1.
Skipping this genome
Not proteome size available for GCA_000808375.1.
Skipping this genome
Not proteome size available for GCA_016949095.1.
Skipping this genome
Not proteome size available for GCA_016950225.1.
Skipping this genome
Not proteome size available for GCA_001039065.1.
Skipping this genome
Not proteome size available for GCA_016950275.1.
Skipping this genome
Not proteome size available for GCA_022507245.1.
Skipping this genome
Not proteome size available for GCA_000808115.1.
Skipping this genome
Not proteome size available for GCA_003382645.2.
Skipping this genome
Not proteome size available for GCA_016949155.1.
Skipping this genome
Not proteome size available for GCA_014946835.1.
Skipping this genome
Not proteome size available for GCA_002904255.1.
Skipping this genome
Not proteome size available for GCA_016949975.1.
Skipping this genome
Not proteome size available for GCA_016949695.1.
Skipping this genome
Not proteome size available for GCA_024342945.1.
Skipping this genome
Not proteome size available for GCA_022507285.1.
Skipping this genome
Not proteome size available for GCA_020406635.1.
Skipping this genome
Not proteome size available for GCA_016949595.1.
Skipping this genome
Not proteome size available for GCA_000808315.1.
Skipping this genome
Not proteome size available for GCA_000808255.1.
Skipping this genome
Not proteome size available for GCA_016949705.1.
Skipping this genome
Not proteome size available for GCA_024506455.1.
Skipping this genome
Not proteome size available for GCA_004137815.1.
Skipping this genome
Not proteome size available for GCA_016949875.1.
Skipping this genome
Not proteome size available for GCA_016107135.1.
Skipping this genome
Not proteome size available for GCA_000808245.1.
Skipping this genome
Not proteome size available for GCA_003312345.1.
Skipping this genome
Not proteome size available for GCA_016949915.1.
Skipping this genome
Not proteome size available for GCA_020295515.1.
Skipping this genome
Not proteome size available for GCA_002930535.1.
Skipping this genome
Not proteome size available for GCA_022507295.1.
Skipping this genome
Not proteome size available for GCA_013913585.1.
Skipping this genome
Not proteome size available for GCA_019426325.1.
Skipping this genome
Not proteome size available for GCA_024343715.1.
Skipping this genome
Not proteome size available for GCA_922011615.1.
Skipping this genome
Not proteome size available for GCA_003595065.1.
Skipping this genome
Not proteome size available for GCA_016988595.1.
Skipping this genome
Not proteome size available for GCA_004296685.1.
Skipping this genome
Not proteome size available for GCA_017255075.1.
Skipping this genome
Not proteome size available for GCA_003990525.1.
Skipping this genome
Not proteome size available for GCA_024343235.1.
Skipping this genome
Not proteome size available for GCA_018094705.1.
Skipping this genome
Not proteome size available for GCA_003594995.1.
Skipping this genome
Not proteome size available for GCA_024342805.1.
Skipping this genome
Not proteome size available for GCA_000808215.1.
Skipping this genome
Not proteome size available for GCA_004137795.1.
Skipping this genome
Not proteome size available for GCA_902143335.1.
Skipping this genome
Not proteome size available for GCA_013449485.1.
Skipping this genome
Not proteome size available for GCA_922016725.1.
Skipping this genome
Not proteome size available for GCA_016107125.1.
Skipping this genome
Not proteome size available for GCA_020181375.1.
Skipping this genome
Not proteome size available for GCA_002847355.1.
Skipping this genome
Not proteome size available for GCA_003312355.1.
Skipping this genome
Not proteome size available for GCA_004296765.1.
Skipping this genome
Not proteome size available for GCA_025466265.1.
Skipping this genome
Not proteome size available for GCA_016949345.1.
Skipping this genome
Not proteome size available for GCA_024343475.1.
Skipping this genome
Not proteome size available for GCA_003096475.1.
Skipping this genome
Not proteome size available for GCA_024343595.1.
Skipping this genome
Not proteome size available for GCA_016950115.1.
Skipping this genome
Not proteome size available for GCA_005771575.1.
Skipping this genome
Not proteome size available for GCA_000738105.1.
Skipping this genome
Not proteome size available for GCA_003628015.1.
Skipping this genome
Not proteome size available for GCA_023507725.1.
Skipping this genome
Not proteome size available for GCA_016107165.1.
Skipping this genome
Not proteome size available for GCA_922022005.1.
Skipping this genome
Not proteome size available for GCA_002983505.1.
Skipping this genome
Not proteome size available for GCA_026723725.1.
Skipping this genome
Not proteome size available for GCA_018095765.1.
Skipping this genome
Not proteome size available for GCA_922013185.1.
Skipping this genome
Not proteome size available for GCA_016949335.1.
Skipping this genome
Not proteome size available for GCA_021614795.1.
Skipping this genome
Not proteome size available for GCA_024499035.1.
Skipping this genome
Not proteome size available for GCA_900095885.1.
Skipping this genome
Not proteome size available for GCA_018361165.1.
Skipping this genome
Not proteome size available for GCA_018361105.1.
Skipping this genome
Not proteome size available for GCA_018904205.1.
Skipping this genome
Not proteome size available for GCA_027920785.2.
Skipping this genome
Not proteome size available for GCA_018361045.1.
Skipping this genome
Not proteome size available for GCA_018361225.1.
Skipping this genome
Not proteome size available for GCA_017161585.1.
Skipping this genome
Not proteome size available for GCA_018361125.1.
Skipping this genome
Not proteome size available for GCA_022871065.1.
Skipping this genome
Not proteome size available for GCA_002846975.1.
Skipping this genome
Not proteome size available for GCA_013334125.1.
Skipping this genome
Not proteome size available for GCA_003628775.1.
Skipping this genome
Not proteome size available for GCA_001187975.3.
Skipping this genome
Not proteome size available for GCA_018361205.1.
Skipping this genome
Not proteome size available for GCA_002887555.1.
Skipping this genome
Not proteome size available for GCA_019464635.1.
Skipping this genome
Not proteome size available for GCA_002706485.1.
Skipping this genome
Not proteome size available for GCA_018361085.1.
Skipping this genome
Not proteome size available for GCA_013334145.1.
Skipping this genome
Not proteome size available for GCA_018361145.1.
Skipping this genome
Not proteome size available for GCA_018361185.1.
Skipping this genome
Not proteome size available for GCA_002812485.1.
Skipping this genome
Not proteome size available for GCA_000758345.1.
Skipping this genome
Not proteome size available for GCA_002846995.1.
Skipping this genome
Not proteome size available for GCA_018361065.1.
Skipping this genome
Not proteome size available for GCA_003718335.1.
Skipping this genome
Not proteome size available for GCA_000023565.1.
Skipping this genome
Not proteome size available for GCA_023212925.1.
Skipping this genome
Not proteome size available for GCA_014854775.1.
Skipping this genome
Not proteome size available for GCA_000147055.1.
Skipping this genome
Not proteome size available for GCA_018628895.1.
Skipping this genome
Not proteome size available for GCA_014854755.1.
Skipping this genome
Not proteome size available for GCA_022385255.1.
Skipping this genome
Not proteome size available for GCA_012278405.1.
Skipping this genome
Not proteome size available for GCA_001644705.1.
Skipping this genome
Not proteome size available for GCA_003403135.1.
Skipping this genome
Not proteome size available for GCA_000025065.1.
Skipping this genome
Not proteome size available for GCA_019464615.1.
Skipping this genome
Not proteome size available for GCA_013334165.1.
Skipping this genome
Not proteome size available for GCA_020047155.1.
Skipping this genome
Not proteome size available for GCA_012278555.1.
Skipping this genome
Not proteome size available for GCA_000816045.1.
Skipping this genome
Not proteome size available for GCA_020520245.1.
Skipping this genome
Not proteome size available for GCA_009873225.2.
Skipping this genome
Not proteome size available for GCA_014893095.1.
Skipping this genome
Not proteome size available for GCA_013334185.1.
Skipping this genome
Not proteome size available for GCA_007858975.2.
Skipping this genome
Not proteome size available for GCA_000382585.2.
Skipping this genome
Not proteome size available for GCA_021614775.1.
Skipping this genome
Not proteome size available for GCA_000831935.2.
Skipping this genome
Not proteome size available for GCA_024971955.1.
Skipping this genome
Not proteome size available for GCA_018361245.1.
Skipping this genome
Not proteome size available for GCA_000511285.2.
Skipping this genome
Not proteome size available for GCA_027920765.2.
Skipping this genome
Not proteome size available for GCA_020406975.1.
Skipping this genome
Not proteome size available for GCA_016404925.1.
Skipping this genome
Not proteome size available for GCA_003121805.1.
Skipping this genome
Not proteome size available for GCA_022748465.1.
Skipping this genome
Not proteome size available for GCA_016106925.1.
Skipping this genome
Not proteome size available for GCA_000400525.1.
Skipping this genome
Not proteome size available for GCA_022748395.1.
Skipping this genome
Not proteome size available for GCA_019739115.1.
Skipping this genome
Not proteome size available for GCA_000400795.1.
Skipping this genome
Not proteome size available for GCA_016106815.1.
Skipping this genome
Not proteome size available for GCA_022748525.1.
Skipping this genome
Not proteome size available for GCA_000406325.1.
Skipping this genome
Not proteome size available for GCA_000774065.1.
Skipping this genome
Not proteome size available for GCA_003049785.1.
Skipping this genome
Not proteome size available for GCA_018596255.1.
Skipping this genome
Not proteome size available for GCA_022747695.1.
Skipping this genome
Not proteome size available for GCA_000406105.1.
Skipping this genome
Not proteome size available for GCA_013168465.1.
Skipping this genome
Not proteome size available for GCA_020406675.1.
Skipping this genome
Not proteome size available for GCA_022748065.1.
Skipping this genome
Not proteome size available for GCA_022748045.1.
Skipping this genome
Not proteome size available for GCA_016106725.1.
Skipping this genome
Not proteome size available for GCA_016106935.1.
Skipping this genome
Not proteome size available for GCA_003600975.1.
Skipping this genome
Not proteome size available for GCA_003595005.1.
Skipping this genome
Not proteome size available for GCA_009874415.1.
Skipping this genome
Not proteome size available for GCA_013168455.1.
Skipping this genome
Not proteome size available for GCA_026891415.1.
Skipping this genome
Not proteome size available for GCA_000406205.1.
Skipping this genome
Not proteome size available for GCA_002921195.1.
Skipping this genome
Not proteome size available for GCA_002924265.1.
Skipping this genome
Not proteome size available for GCA_003600995.1.
Skipping this genome
Not proteome size available for GCA_017897245.1.
Skipping this genome
Not proteome size available for GCA_013339875.1.
Skipping this genome
Not proteome size available for GCA_000406185.1.
Skipping this genome
Not proteome size available for GCA_026891295.1.
Skipping this genome
Not proteome size available for GCA_013339795.1.
Skipping this genome
Not proteome size available for GCA_018596245.1.
Skipping this genome
Not proteome size available for GCA_009873535.1.
Skipping this genome
Not proteome size available for GCA_020406815.1.
Skipping this genome
Not proteome size available for GCA_016404945.1.
Skipping this genome
Not proteome size available for GCA_000406305.1.
Skipping this genome
Not proteome size available for GCA_017897585.1.
Skipping this genome
Not proteome size available for GCA_000803195.1.
Skipping this genome
Not proteome size available for GCA_016106795.1.
Skipping this genome
Not proteome size available for GCA_000975305.1.
Skipping this genome
Not proteome size available for GCA_000406045.1.
Skipping this genome
Not proteome size available for GCA_013168475.1.
Skipping this genome
Not proteome size available for GCA_000406225.1.
Skipping this genome
Not proteome size available for GCA_003934295.1.
Skipping this genome
Not proteome size available for GCA_016106895.1.
Skipping this genome
Not proteome size available for GCA_000365305.1.
Skipping this genome
Not proteome size available for GCA_000406085.2.
Skipping this genome
Not proteome size available for GCA_022748425.1.
Skipping this genome
Not proteome size available for GCA_018596285.1.
Skipping this genome
Not proteome size available for GCA_017897605.1.
Skipping this genome
Not proteome size available for GCA_001401695.1.
Skipping this genome
Not proteome size available for GCA_013339815.1.
Skipping this genome
Not proteome size available for GCA_022747985.1.
Skipping this genome
Not proteome size available for GCA_000406145.1.
Skipping this genome
Not proteome size available for GCA_016106945.1.
Skipping this genome
Not proteome size available for GCA_019444095.1.
Skipping this genome
Not proteome size available for GCA_001187965.2.
Skipping this genome
Not proteome size available for GCA_022748005.1.
Skipping this genome
Not proteome size available for GCA_022748385.1.
Skipping this genome
Not proteome size available for GCA_022747795.1.
Skipping this genome
Not proteome size available for GCA_000784725.1.
Skipping this genome
Not proteome size available for GCA_025643375.1.
Skipping this genome
Not proteome size available for GCA_022748125.1.
Skipping this genome
Not proteome size available for GCA_009874405.1.
Skipping this genome
Not proteome size available for GCA_009874305.1.
Skipping this genome
Not proteome size available for GCA_000430955.1.
Skipping this genome
Not proteome size available for GCA_016404895.1.
Skipping this genome
Not proteome size available for GCA_000406125.1.
Skipping this genome
Not proteome size available for GCA_000406065.1.
Skipping this genome
Not proteome size available for GCA_009873565.1.
Skipping this genome
Not proteome size available for GCA_000406165.1.
Skipping this genome
Not proteome size available for GCA_017656535.1.
Skipping this genome
Not proteome size available for GCA_000404105.1.
Skipping this genome
Not proteome size available for GCA_000400565.1.
Skipping this genome
Not proteome size available for GCA_020406685.1.
Skipping this genome
Not proteome size available for GCA_003595075.1.
Skipping this genome
Not proteome size available for GCA_009903315.1.
Skipping this genome
Not proteome size available for GCA_012273755.1.
Skipping this genome
Not proteome size available for GCA_001417915.1.
Skipping this genome
Not proteome size available for GCA_000406245.1.
Skipping this genome
Not proteome size available for GCA_022748025.1.
Skipping this genome
Not proteome size available for GCA_029320885.1.
Skipping this genome
Not proteome size available for GCA_024124775.1.
Skipping this genome
Not proteome size available for GCA_003049805.1.
Skipping this genome
Not proteome size available for GCA_009873515.1.
Skipping this genome
Not proteome size available for GCA_000365345.1.
Skipping this genome
Not proteome size available for GCA_009874395.1.
Skipping this genome
Not proteome size available for GCA_020406895.1.
Skipping this genome
Not proteome size available for GCA_014751545.1.
Skipping this genome
Not proteome size available for GCA_013168495.1.
Skipping this genome
Not proteome size available for GCA_022748105.1.
Skipping this genome
Not proteome size available for GCA_026891395.1.
Skipping this genome
Not proteome size available for GCA_016404885.1.
Skipping this genome
Not proteome size available for GCA_000365405.2.
Skipping this genome
Not proteome size available for GCA_020406575.1.
Skipping this genome
Not proteome size available for GCA_022747825.1.
Skipping this genome
Not proteome size available for GCA_022747745.1.
Skipping this genome
Not proteome size available for GCA_013168575.1.
Skipping this genome
Not proteome size available for GCA_000474655.1.
Skipping this genome
Not proteome size available for GCA_016106805.1.
Skipping this genome
Not proteome size available for GCA_001401705.2.
Skipping this genome
Not proteome size available for GCA_000406285.1.
Skipping this genome
Not proteome size available for GCA_020406775.1.
Skipping this genome
Not proteome size available for GCA_022747785.1.
Skipping this genome
Not proteome size available for GCA_003725575.1.
Skipping this genome
Not proteome size available for GCA_009874325.1.
Skipping this genome
Not proteome size available for GCA_009372235.1.
Skipping this genome
Not proteome size available for GCA_000365365.1.
Skipping this genome
Not proteome size available for GCA_022748365.1.
Skipping this genome
Not proteome size available for GCA_000365285.1.
Skipping this genome
Not proteome size available for GCA_016106885.1.
Skipping this genome
Not proteome size available for GCA_026891515.1.
Skipping this genome
Not proteome size available for GCA_022747755.1.
Skipping this genome
Not proteome size available for GCA_017897305.1.
Skipping this genome
Not proteome size available for GCA_016106695.1.
Skipping this genome
Not proteome size available for GCA_024124785.1.
Skipping this genome
Not proteome size available for GCA_016106855.1.
Skipping this genome
Not proteome size available for GCA_007210685.1.
Skipping this genome
Not proteome size available for GCA_016106995.1.
Skipping this genome
Not proteome size available for GCA_016106685.1.
Skipping this genome
Not proteome size available for GCA_009874285.1.
Skipping this genome
Not proteome size available for GCA_016106985.1.
Skipping this genome
Not proteome size available for GCA_022748555.1.
Skipping this genome
Not proteome size available for GCA_002906725.1.
Skipping this genome
Not proteome size available for GCA_018596235.1.
Skipping this genome
Not proteome size available for GCA_009873555.1.
Skipping this genome
Not proteome size available for GCA_001995275.1.
Skipping this genome
Not proteome size available for GCA_026891405.1.
Skipping this genome
Not proteome size available for GCA_022750655.1.
Skipping this genome
Not proteome size available for GCA_022747725.1.
Skipping this genome
Not proteome size available for GCA_027571425.1.
Skipping this genome
Not proteome size available for GCA_016106785.1.
Skipping this genome
Not proteome size available for GCA_016106715.1.
Skipping this genome
Not proteome size available for GCA_003725615.1.
Skipping this genome
Not proteome size available for GCA_000784735.1.
Skipping this genome
Not proteome size available for GCA_029635795.1.
Skipping this genome
Not proteome size available for GCA_001506125.1.
Skipping this genome
Not proteome size available for GCA_022748085.1.
Skipping this genome
Not proteome size available for GCA_013168485.1.
Skipping this genome
Not proteome size available for GCA_022747685.1.
Skipping this genome
Not proteome size available for GCA_014642955.1.
Skipping this genome
Not proteome size available for GCA_013339845.1.
Skipping this genome
Not proteome size available for GCA_000406265.1.
Skipping this genome
Not proteome size available for GCA_000023545.1.
Skipping this genome
Not proteome size available for GCA_022761095.1.
Skipping this genome
Not proteome size available for GCA_014855505.1.
Skipping this genome
Not proteome size available for GCA_000400505.1.
Skipping this genome
Not proteome size available for GCA_002291445.1.
Skipping this genome
Not proteome size available for GCA_017569925.1.
Skipping this genome
Not proteome size available for GCA_005484965.1.
Skipping this genome
Not proteome size available for GCA_005484945.1.
Skipping this genome
Not proteome size available for GCA_003666235.1.
Skipping this genome
Not proteome size available for GCA_003666165.1.
Skipping this genome
Not proteome size available for GCA_022358715.1.
Skipping this genome
Not proteome size available for GCA_022358635.1.
Skipping this genome
Not proteome size available for GCA_003666145.1.
Skipping this genome
Not proteome size available for GCA_022358755.1.
Skipping this genome
Not proteome size available for GCA_012932875.1.
Skipping this genome
Not proteome size available for GCA_013155275.1.
Skipping this genome
Not proteome size available for GCA_022358675.1.
Skipping this genome
Not proteome size available for GCA_003315515.1.
Skipping this genome
Not proteome size available for GCA_025840185.1.
Skipping this genome
Not proteome size available for GCA_022358645.1.
Skipping this genome
Not proteome size available for GCA_022361495.1.
Skipping this genome
Not proteome size available for GCA_025840205.1.
Skipping this genome
Not proteome size available for GCA_000225565.1.
Skipping this genome
Not proteome size available for GCA_022359025.1.
Skipping this genome
Not proteome size available for GCA_022358805.1.
Skipping this genome
Not proteome size available for GCA_022358855.1.
Skipping this genome
Not proteome size available for GCA_003115855.1.
Skipping this genome
Not proteome size available for GCA_016456245.1.
Skipping this genome
Not proteome size available for GCA_022359135.1.
Skipping this genome
Not proteome size available for GCA_001049335.1.
Skipping this genome
Not proteome size available for GCA_003666245.1.
Skipping this genome
Not proteome size available for GCA_023347555.1.
Skipping this genome
Not proteome size available for GCA_023347585.1.
Skipping this genome
Not proteome size available for GCA_003115835.1.
Skipping this genome
Not proteome size available for GCA_023347575.1.
Skipping this genome
Not proteome size available for GCA_003115815.1.
Skipping this genome
Not proteome size available for GCA_003115845.1.
Skipping this genome
Not proteome size available for GCA_003515985.1.
Skipping this genome
Not proteome size available for GCA_015999465.1.
Skipping this genome
Not proteome size available for GCA_002111625.1.
Skipping this genome
Not proteome size available for GCA_003269815.1.
Skipping this genome
Not proteome size available for GCA_003269585.1.
Skipping this genome
Not proteome size available for GCA_946477055.1.
Skipping this genome
Not proteome size available for GCA_900107885.1.
Skipping this genome
Not proteome size available for GCA_003269565.1.
Skipping this genome
Not proteome size available for GCA_003269775.1.
Skipping this genome
Not proteome size available for GCA_002111635.1.
Skipping this genome
Not proteome size available for GCA_003269655.1.
Skipping this genome
Not proteome size available for GCA_024138995.1.
Skipping this genome
Not proteome size available for GCA_002111545.1.
Skipping this genome
Not proteome size available for GCA_003730085.1.
Skipping this genome
Not proteome size available for GCA_003269855.1.
Skipping this genome
Not proteome size available for GCA_003269735.1.
Skipping this genome
Not proteome size available for GCA_000688675.1.
Skipping this genome
Not proteome size available for GCA_003269485.1.
Skipping this genome
Not proteome size available for GCA_003269905.1.
Skipping this genome
Not proteome size available for GCA_002111595.1.
Skipping this genome
Not proteome size available for GCA_003269575.1.
Skipping this genome
Not proteome size available for GCA_003730055.1.
Skipping this genome
Not proteome size available for GCA_003269835.1.
Skipping this genome
Not proteome size available for GCA_003269595.1.
Skipping this genome
Not proteome size available for GCA_003269645.1.
Skipping this genome
Not proteome size available for GCA_003269505.1.
Skipping this genome
Not proteome size available for GCA_003269665.1.
Skipping this genome
Not proteome size available for GCA_003269725.1.
Skipping this genome
Not proteome size available for GCA_003269805.1.
Skipping this genome
Not proteome size available for GCA_000688695.1.
Skipping this genome
Not proteome size available for GCA_002111555.1.
Skipping this genome
Not proteome size available for GCA_003269765.1.
Skipping this genome
Not proteome size available for GCA_003269515.1.
Skipping this genome
Not proteome size available for GCA_003269495.1.
Skipping this genome
Not proteome size available for GCA_003730035.1.
Skipping this genome
Not proteome size available for GCA_003269875.1.
Skipping this genome
Not proteome size available for GCA_000688655.1.
Skipping this genome
Not proteome size available for GCA_003269685.1.
Skipping this genome
Not proteome size available for GCA_002111585.1.
Skipping this genome
Not proteome size available for GCA_010131535.1.
Skipping this genome
Not proteome size available for GCA_008710095.1.
Skipping this genome
Not proteome size available for GCA_004342665.1.
Skipping this genome

Calc proteome perc: 0it [00:00, ?it/s]

	Genus	MeanProteomeSize	SdProteomeSize	MeanProteomePerc	SdProteomePerc	NumOfGenomes

pectobact_average = ['Pectobacteriaceae']
for col in proteome_perc_df.columns[1:]:
    pectobact_average.append(np.mean(list(proteome_perc_df[col])))
pectobact_average[-1] == 660

df = pd.DataFrame([pectobact_average], columns=proteome_perc_df.columns)
pd.concat([proteome_perc_df, df], ignore_index=True, axis=0).round(2)

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.
  return _methods._mean(a, axis=axis, dtype=dtype,
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)

	Genus	MeanProteomeSize	SdProteomeSize	MeanProteomePerc	SdProteomePerc	NumOfGenomes
0	Pectobacteriaceae	NaN	NaN	NaN	NaN	NaN

Combine into a single dataframe

For easier comparison and presentation, combine the dataframes made above into a single dataframe, with each row representing a different genus.

all_df = pd.concat([proteome_perc_df, cazome_sizes_df, cazome_fams_df, cazome_ratio_df], axis=1, join='inner')
make_output_directory(Path('../results/cazome_size'), force=True, nodelete=True)
all_df.to_csv('../results/cazome_size/cazome_sizes.csv')
all_df

Built output directory: ../results/cazome_size

	Genus	MeanProteomeSize	SdProteomeSize	MeanProteomePerc	SdProteomePerc	NumOfGenomes	Genus	MeanProteins	SdProteins	NumOfGenomes	Genus	MeanFamilys	SdFamilys	NumOfGenomes	Genus	MeanCAZymeToFamRatio	SdCAZymeToFamRatio	NumOfGenomes

# calculate means for Pectobacteriaceae
for col in all_df:
    if col == 'Genus' or col == 'NumOfGenomes':
        continue
    print(col, '--', np.mean(list(all_df[col])).round(2))

MeanProteomeSize -- nan
SdProteomeSize -- nan
MeanProteomePerc -- nan
SdProteomePerc -- nan
MeanProteins -- nan
SdProteins -- nan
MeanFamilys -- nan
SdFamilys -- nan
MeanCAZymeToFamRatio -- nan
SdCAZymeToFamRatio -- nan

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.
  return _methods._mean(a, axis=axis, dtype=dtype,
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)

2. CAZy classes

Calculate the number of CAZymes (identified as the number of unique protein accessions) per CAZy class. Also, calculate the mean size of CAZy classes (i.e. the mean number of unique protein accessions per CAZy class in each genome) per genus.

The results are added to a dataframe, which is written to results/pecto_dic/cazy_class_sizes.csv, and was used to generate a proportiona area plot using RawGraphs.

# make output dir for results, will not delete data if dir already exists
make_output_directory(Path('../results/cazy_classes/'), force=True, nodelete=True)

Built output directory: ../results/cazy_classes

class_df, class_size_dict = calculate_class_sizes(fgp_df, 'Genus', round_by=2)

Getting CAZy class sizes: 100%|██████████| 83143/83143 [00:36<00:00, 2285.77it/s]
Calculating CAZy class sizes: 100%|██████████| 6/6 [00:00<00:00, 134.29it/s]

# add values with means across all genera to represent pectobacteriaceae
pectobact_class_means = []

for cazy_class in set(class_df['CAZyClass']):
    df = class_df[class_df['CAZyClass'] == cazy_class]
    new_row = [cazy_class, 'Pectobacteriaceae']
    
    for col in class_df.columns[2:]:
        mean = np.mean(df[col])
        new_row.append(mean)
    
    new_row[-1] = 660
    
    pectobact_class_means.append(new_row)

df = pd.DataFrame(pectobact_class_means, columns = class_df.columns)
all_class_df = pd.concat([class_df, df], axis=0, ignore_index=True)
all_class_df = all_class_df.round(2)
# replace nan with 0
all_class_df = all_class_df.fillna(0)

filtered_class_df = all_class_df[all_class_df['Genus'] != 'Haf']
all_class_df.to_csv('../results/cazy_classes/cazy_class_sizes.csv')

all_class_df

	CAZyClass	Genus	MeanCazyClass	SdCazyClass	MeanClassPerc	SdClassPerc	NumOfGenomes
0	GH	Dickeya	42.53	3.55	38.24	1.85	206
1	GH	Pectobacterium	50.11	3.91	44.50	1.87	432
2	GH	Brenneria	42.48	6.60	48.14	4.19	33
3	GH	Affinibrenneria	59.00	0.00	54.63	0.00	1
4	GH	Samsonia	34.00	0.00	41.98	0.00	1
5	GH	Lonsdalea	30.85	2.28	39.96	1.31	39
6	GH	Acerihabitans	51.00	0.00	48.11	0.00	1
7	GH	Musicola	37.00	0.00	40.13	0.99	4
8	GT	Dickeya	37.21	3.12	33.52	2.62	206
9	GT	Pectobacterium	31.76	3.86	28.15	2.23	432
10	GT	Brenneria	32.30	2.55	36.90	2.61	33
11	GT	Affinibrenneria	35.00	0.00	32.41	0.00	1
12	GT	Samsonia	25.00	0.00	30.86	0.00	1
13	GT	Lonsdalea	32.46	2.30	42.07	1.24	39
14	GT	Acerihabitans	44.00	0.00	41.51	0.00	1
15	GT	Musicola	31.00	3.00	33.55	2.44	4
16	PL	Dickeya	16.29	1.72	14.63	1.19	206
17	PL	Pectobacterium	14.78	1.36	13.14	0.97	432
18	PL	Brenneria	4.24	1.23	4.81	1.29	33
19	PL	Affinibrenneria	1.00	0.00	0.93	0.00	1
20	PL	Samsonia	8.00	0.00	9.88	0.00	1
21	PL	Lonsdalea	3.79	0.56	4.92	0.72	39
22	PL	Acerihabitans	1.00	0.00	0.94	0.00	1
23	PL	Musicola	11.25	0.43	12.19	0.33	4
24	CE	Dickeya	7.16	0.80	6.44	0.60	206
25	CE	Pectobacterium	7.12	0.83	6.33	0.68	432
26	CE	Brenneria	4.30	1.06	4.93	1.24	33
27	CE	Affinibrenneria	7.00	0.00	6.48	0.00	1
28	CE	Samsonia	8.00	0.00	9.88	0.00	1
29	CE	Lonsdalea	3.15	0.48	4.09	0.57	39
30	CE	Acerihabitans	5.00	0.00	4.72	0.00	1
31	CE	Musicola	6.00	0.00	6.51	0.16	4
32	AA	Dickeya	1.00	0.00	0.90	0.06	85
33	AA	Pectobacterium	1.03	0.16	0.91	0.17	371
34	AA	Brenneria	1.00	0.00	1.27	0.06	8
35	AA	Affinibrenneria	0.00	0.00	0.00	0.00	1
36	AA	Samsonia	1.00	0.00	1.23	0.00	1
37	AA	Lonsdalea	0.00	0.00	0.00	0.00	39
38	AA	Acerihabitans	0.00	0.00	0.00	0.00	1
39	AA	Musicola	0.00	0.00	0.00	0.00	4
40	CBM	Dickeya	12.27	1.25	11.03	0.83	206
41	CBM	Pectobacterium	13.98	1.49	12.41	1.02	432
42	CBM	Brenneria	9.36	0.64	10.74	1.17	33
43	CBM	Affinibrenneria	10.00	0.00	9.26	0.00	1
44	CBM	Samsonia	10.00	0.00	12.35	0.00	1
45	CBM	Lonsdalea	8.74	0.54	11.35	0.63	39
46	CBM	Acerihabitans	11.00	0.00	10.38	0.00	1
47	CBM	Musicola	11.00	1.00	11.96	1.38	4
48	AA	Pectobacteriaceae	0.50	0.02	0.54	0.04	660
49	CBM	Pectobacteriaceae	10.79	0.62	11.18	0.63	660
50	PL	Pectobacteriaceae	7.54	0.66	7.68	0.56	660
51	GH	Pectobacteriaceae	43.37	2.04	44.46	1.28	660
52	CE	Pectobacteriaceae	5.97	0.40	6.17	0.41	660
53	GT	Pectobacteriaceae	33.59	1.85	34.87	1.39	660

Explore AA CAZymes in Pectobacteriaceae

Very few genomes contained any AA CAZymes.

Identify the number of genomes were no AA CAZymes were found, additionally, find the maximum, minimum and mode number of AA CAZymes found across all 660 genomes.

# calc genomes with no AAs
no_aa_genomes = 0
for genus in class_size_dict['AA']:
    for genome in class_size_dict['AA'][genus]:
        no_aa_genomes+=1
print(f"{no_aa_genomes} genomes have no AAs")

aa_counts = [0] * no_aa_genomes
for genus in class_size_dict['AA']:
    for genome in class_size_dict['AA'][genus]:
        aa_counts.append(len(class_size_dict['AA'][genus][genome]['proteins']))
print(f"Max: {max(aa_counts)}\nMin: {min(aa_counts)}\nMode: {statistics.mode(aa_counts)}")

465 genomes have no AAs
Max: 2
Min: 0
Mode: 0

Count the number of genomes were 1 or 2 AA CAZymes were found.

# find genomes with 2 AAs
two_aa_genomes = {}
one_aa_genomes = {}

for genus in class_size_dict['AA']:
    for genome in class_size_dict['AA'][genus]:
        if len(class_size_dict['AA'][genus][genome]['proteins']) == 2:
            try:
                two_aa_genomes[genus].add(genome)
            except KeyError:
                two_aa_genomes[genus] = {genome}
                
        elif len(class_size_dict['AA'][genus][genome]['proteins']) == 1:
            try:
                one_aa_genomes[genus].add(genome)
            except KeyError:
                one_aa_genomes[genus] = {genome}

two_aa_genomes

{'Pectobacterium': {'GCA_000738125.1',
  'GCA_000749915.1',
  'GCA_011378985.1',
  'GCA_011379045.1',
  'GCA_020971565.1',
  'GCA_024343355.1',
  'GCA_024722495.1',
  'GCA_028335745.1',
  'GCA_900195285.2',
  'GCA_900195295.2'}}

for genus in one_aa_genomes:
    print(f"{genus}: {len(one_aa_genomes[genus])}")

Pectobacterium: 361
Dickeya: 85
Brenneria: 8
Samsonia: 1

3. CAZy families

CAZy family frequency dataframe

Calculate the number of CAZymes per CAZy family presented in each genome, where the number of CAZymes is the number of unqiue protein accessions. This value may be greater than the number of CAZymes in the genome because a CAZyme may be annotated with multiple CAZy families.

# make output directory
# make output dir for results, will not delete data if dir already exists
make_output_directory(Path('../results/cazy_families/'), force=True, nodelete=True)

Built output directory: ../results/cazy_families

fam_freq_df = build_fam_freq_df(fgp_df, ['Genus', 'Species'])
fam_freq_df

The dataset contains 117 CAZy families

Counting fam frequencies: 100%|██████████| 717/717 [01:04<00:00, 11.10it/s]

	Genome	Genus	Species	AA10	AA3	CBM0	CBM13	CBM18	CBM3	CBM32	...	PL11	PL17	PL2	PL22	PL26	PL3	PL35	PL38	PL4	PL9
0	GCA_005484945.1	Brenneria	rubrifaciens	0	1	0	0	0	1	0	...	0	0	0	0	0	1	0	0	0	0
1	GCA_003121725.1	Pectobacterium	atrosepticum	0	1	0	1	0	1	1	...	0	0	2	1	1	2	0	0	1	2
2	GCA_020406855.1	Pectobacterium	carotovorum	0	1	0	1	0	1	1	...	1	0	2	1	1	2	0	0	1	2
3	GCA_003121785.1	Pectobacterium	parmentieri	0	0	0	1	0	1	1	...	0	0	2	1	1	1	0	0	1	2
4	GCA_022748065.1	Dickeya	dianthicola	0	0	0	0	0	0	1	...	0	0	1	1	1	2	0	0	2	3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
712	GCA_018628895.1	Dickeya	dianthicola	0	0	0	0	0	0	1	...	0	0	1	1	1	2	0	1	2	3
713	GCA_019426305.1	Pectobacterium	polaris	0	1	0	1	0	1	2	...	1	0	2	1	1	2	0	0	1	2
714	GCA_922037565.1	Pectobacterium	polaris	0	1	0	1	0	1	2	...	1	0	2	1	1	1	0	0	1	2
715	GCA_003595065.1	Pectobacterium	versatile	0	1	0	1	0	1	1	...	0	0	2	1	1	2	0	0	1	2
716	GCA_001742185.1	Pectobacterium	wasabiae CFBP 3304	0	0	0	0	0	1	1	...	0	0	2	1	1	1	0	1	1	1

717 rows × 120 columns

fam_freq_df.to_csv("../results/cazy_families/cazy_fam_freqs.csv")

Clustermaps

Build clustermap of CAZy family frequencies, with additional row colours marking the genus classification of each genome (i.e. each row).

Prepare the dataframe of CAZy family frequencies

Index fam_freq_df so that each row name contains the genome, Genus and Species, so that the genomic accession, genus and species is included in the clustermap.

# index the taxonomy data and genome (ggs=genome_genus_species)
fam_freq_df_ggs = copy(fam_freq_df)  # so does not alter fam_freq_df
fam_freq_df_ggs = fam_freq_df_ggs.set_index(['Genome','Genus','Species'])
fam_freq_df_ggs.head(1)

			AA10	AA3	CBM0	CBM13	CBM18	CBM3	CBM32	CBM4	CBM48	CBM5	...	PL11	PL17	PL2	PL22	PL26	PL3	PL35	PL38	PL4	PL9
Genome	Genus	Species
GCA_005484945.1	Brenneria	rubrifaciens	0	1	0	0	0	1	0	0	1	1	...	0	0	0	0	0	1	0	0	0	0

1 rows × 117 columns

Colour scheme

Define a colour scheme to colour code the rows by, in this case by the genus of the species.

To do this, add a column containing the data to be used to colour code each row, e.g. a genus. This extra column is removed by build_row_colours().

Note: The dataframe that is parsed to build_row_colours()<\b> must be the dataframe that is used to generate a clustermap, otherwise Seaborn will not be able to map the row oclours correctly and no row colours will be produced.

# define a colour scheme to colour code rows by genus
fam_freq_df_ggs['Genus'] = list(fam_freq_df['Genus'])  # add column to use for colour scheme, is removed
fam_freq_genus_row_colours, fam_g_lut = build_row_colours(fam_freq_df_ggs, 'Genus', 'Set2')

Build a clustermap of CAZy family frequencies

Use the function build_family_clustermap() from cazomevolve to build clustermaps of the CAZy family frequencies, with different combinations of additional row colours. For example, the row colours could list the genus and/or species classification of each genome.

# make a figure that is full size, and all data is legible
print("""
A large version of a cluster map of CAZy family frequencies with each row representing a unique 
genome (and colour coded by the genus classification of the genome), and each column representing 
a unique CAZy family.

This full sized figure is generated for human readability. However, this figure is extremely large, and 
potentially too large for publication. Therefore, a smaller figure is generated below.
""")
large_fam_clustermap = build_family_clustermap(
    fam_freq_df_ggs,
    row_colours=fam_freq_genus_row_colours,
    fig_size=(40,120),
    file_path="../results/cazy_families/fam_freq_clustermap.svg",
    file_format='svg',
    lut=fam_g_lut,
    legend_title='Genus',
    dendrogram_ratio=(0.2,0.05),
    title_fontsize=28,
    legend_fontsize=24,
    cbar_pos=(0, 0.95, 0.05, 0.05),
)

A large version of a cluster map of CAZy family frequencies with each row representing a unique 
genome (and colour coded by the genus classification of the genome), and each column representing 
a unique CAZy family.

This full sized figure is generated for human readability. However, this figure is extremely large, and 
potentially too large for publication. Therefore, a smaller figure is generated below.

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

# make a figure the optimal size to fit in a paper
build_family_clustermap(
    fam_freq_df_ggs,
    row_colours=fam_freq_genus_row_colours,
    fig_size=(20,35),
    file_path="../results/cazy_families/paper_fam_freq_clustermap.png",
    file_format='png',
    font_scale=0.5,
    lut=fam_g_lut,
    legend_title='Genus',
    dendrogram_ratio=(0.1,0.05),
    title_fontsize=18,
    legend_fontsize=16,
    cbar_pos=(0, 0.95, 0.05, 0.05),
)

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

<seaborn.matrix.ClusterGrid at 0x7f0f929554c0>

Add species classifications

Looking at the species names in the clustermap, there appears to be clustering of the genomes in a manner that correlates not only with their genus classificaiton but also their species classification. Therefore, add an additional row of row-colours, marking the species classification of each genome.

# define a colour scheme to colour code rows by SPECIES
fam_freq_df_ggs['Species'] = list(fam_freq_df['Species'])  # add column to use for colour scheme, is removed
fam_freq_species_row_colours, fam_s_lut = build_row_colours(fam_freq_df_ggs, 'Species', 'rainbow')

# make a figure the optimal size to fit in a paper
build_family_clustermap_multi_legend(
    df=fam_freq_df_ggs,
    row_colours=[fam_freq_genus_row_colours,fam_freq_species_row_colours],
    luts=[fam_g_lut, fam_s_lut],
    legend_titles=['Genus', 'Species'],
    bbox_to_anchors=[(0.2,1.045), (0.63,1.04)],
    legend_cols=[1,5],
    fig_size=(20,40),
    file_path="../results/cazy_families/paper_genus_species_fam_freq_clustermap.png",
    file_format='png',
    font_scale=1,
    dendrogram_ratio=(0.1,0.05),
    title_fontsize=18,
    legend_fontsize=16,
    cbar_pos=(0.01, 0.96, 0.1, 0.1),  #left, bottom, width, height
)

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

<seaborn.matrix.ClusterGrid at 0x7f0f903eb700>

Add phenotype classification to clustermap

The clustering of the genomes into their respective genera appears to also correlate with the soft versus hard plant tissue targeting phenotypes. Therefore, add an additional row colour that colour codes each row by the genomes preference for soft or hard plant tissues.

# define a colour scheme to colour code SOFT vs HARD plant tissue targeting genomes
phenotype_col = []
for ri in range(len(fam_freq_df_ggs)):
    if list(fam_freq_df['Genus'])[ri] in ['Pectobacterium', 'Dickeya', 'Musicola']:
        phenotype_col.append('Soft tissue targeting')
    else:
        phenotype_col.append('Hard tissue targeting')
fam_freq_df_ggs['Phenotype'] = phenotype_col
fam_freq_pheno_row_colours, fam_p_lut = build_row_colours(fam_freq_df_ggs, 'Phenotype', "Set1")

build_family_clustermap_multi_legend(
    df=fam_freq_df_ggs,
    row_colours=[fam_freq_pheno_row_colours, fam_freq_genus_row_colours],
    luts=[fam_p_lut, fam_g_lut],
    legend_titles=['Phenotype', 'Genus'],
    bbox_to_anchors=[(0.225,1.045), (0.63,1.04)],
    legend_cols=[1,5],
    fig_size=(27,41),
    file_path="../results/cazy_families/paper_pheno_genus_fam_freq_clustermap.png",
    file_format='png',
    font_scale=0.7,
    dendrogram_ratio=(0.1,0.05),
    title_fontsize=18,
    legend_fontsize=16,
    cbar_pos=(0.01, 0.96, 0.1, 0.1),  #left, bottom, width, height
)

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

<seaborn.matrix.ClusterGrid at 0x7f0f8572d430>

Remove genomes

In the clustermaps the genomes GCA_029023745.1 (Pectobacterium colocasium), GCA_000749925.1 and GCA_000749945.1 (Pectobacterium betavasulorum) contained under estimated representations of their respective CAZomes.

Six Pectobacterium genomes were not included within the main Pectobacterium subtree (dendrogram on the RHS of clustermap):

• GCA_000749925.1 and GCA_000749845.1 (Pectobacterium betavasulorum)
• GCA_000803215.1 (Pectobacterium fontis)
• GCA_025946765.1 (Pectobacterium cacticida)
• GCA_004137815.1 (Pectobacterium zantedeschiae)
• GCA_029023745.1 (Pectobacterium colocasium)

Extracted from the paper:

The genomes appeared to contain fewer total CAZymes (inferred from the lower CAZy family frequencies) than other Pectobacterium genomes, inferring a potential underestimation of their CAZyme features. Genomes GCA_000749925.1, GCA_000749845.1, and GCA_000803215.1 were were listed with the assembly status 'contig' in NCBI (June 2021). Genomic assemblies with the assembly status of 'contig' may contain incomplete genomic sequences. Indeed, the reported CheckM (Parks et al 2015, Genome Res) analysis listed the GCA_000749925.1 and GCA_000749845.1 as missing 5% (100th percentile) of their genomes with 2.25-2.5% contamination, and GCA_000803215.1 as missing 10% (100th percentile). Furthermore, although listed with the assembly status 'complete genome', assembly GCA_025946765.1 was listed as missing 19% (100th percentile) of its genome by CheckM, and the scaffold GCA_004137815.1 was listed as missing 11% (33rd percentile) with 9% contamination. Therefore, the annotated proteomes potentially underestimates the number of features (including CAZymes) in the genomes, and were excluded from the downstream analyses. The genome GCA_029023745.1 was listed with the assembly status 'complete genome', but the NCBI Prokaryotic Genome Annotation Pipeline (PGAGP) output contained a suspiciously high number of frameshifted proteins (greater than 30%), inferring a potentially poor annotation of the genome that may have resulted in an underestimation of its CAZyme features. Therefore, this genome was also excluded from downstream analyses.

genomes_to_remove = [
    'GCA_000749925.1',
    'GCA_000749845.1',
    'GCA_000803215.1',
    'GCA_025946765.1',
    'GCA_004137815.1',
    'GCA_029023745.1',
]
fam_freq_filtered_df = fam_freq_df[~fam_freq_df['Genome'].isin(genomes_to_remove)]
print(f"Original df length: {len(fam_freq_df)}\nLength after removing genome: {len(fam_freq_filtered_df)}")

Original df length: 717
Length after removing genome: 711

Replot the clustermap

Replot the clustermap, exlucding the removed genomes.

fam_freq_filtered_df_ggs = fam_freq_filtered_df.set_index(['Genome', 'Genus', 'Species'])

# make a figure the optimal size to fit in a paper
build_family_clustermap(
    fam_freq_df_ggs,
    row_colours=fam_freq_genus_row_colours,
    fig_size=(20,70),
    file_path="../results/cazy_families/paper_fam_freq_clustermap_FILTERED.svg",
    file_format='svg',
    font_scale=0.5,
    lut=fam_g_lut,
    legend_title='Genus',
    dendrogram_ratio=(0.1,0.05),
    title_fontsize=18,
    legend_fontsize=16,
    cbar_pos=(0, 0.95, 0.05, 0.05),
)

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

<seaborn.matrix.ClusterGrid at 0x7f0f7d1382b0>

Genus specific CAZy families

Identify CAZy families that are only present in one group, e.g. one Genus, using the function get_group_specific_fams from cazomevolve.

Specifically, get_group_specific_fams returns two dicts:

1. Group specific families: {group: {only unique fams}}
2. All families per group: {group: {all fams}}

all_families = list(fam_freq_df.columns)[3:]
# dict {group: {only unique fams}} and dict {group: {all fams}}
unique_grp_fams, group_fams = get_group_specific_fams(fam_freq_filtered_df, 'Genus', all_families)
unique_grp_fams

Identifying fams in each Genus: 100%|██████████| 711/711 [00:17<00:00, 40.48it/s]
Identifying Genus specific fams: 100%|██████████| 8/8 [00:00<00:00, 11790.03it/s]

{'Brenneria': {'GH106', 'GT21', 'PL17'},
 'Pectobacterium': {'AA10',
  'CBM13',
  'GH121',
  'GH146',
  'GH18',
  'GT101',
  'GT102',
  'GT11',
  'GT111',
  'GT14',
  'GT24',
  'GT52',
  'PL11'},
 'Dickeya': {'CBM4', 'CE2', 'GH113', 'GH148', 'GH25', 'GH91', 'GT97', 'PL10'},
 'Acerihabitans': {'GH127', 'GH15'}}

Identify phenotype specific CAZy families

Identify families that are only found in hard plant tissue targeting genomes, and those families only found in soft plant tissue targeting species.

hard_soft_fams_dict = {'hard': set(), 'soft': set()}

for ri in tqdm(range(len(fam_freq_filtered_df)), desc="Identifying Soft and Hard plant tissue targeting families"):
    genus = fam_freq_filtered_df.iloc[ri]['Genus']
    
    if genus in ['Pectobacterium','Dickeya','Musicola']:
        grp = 'soft'
    else:
        grp = 'hard'
    
    for fam in fam_freq_filtered_df.columns[3:]:
        if fam_freq_filtered_df.iloc[ri][fam] > 1:
            hard_soft_fams_dict[grp].add(fam)

unique_hard_fams = hard_soft_fams_dict['hard'].difference(hard_soft_fams_dict['soft'])
unique_soft_fams = hard_soft_fams_dict['soft'].difference(hard_soft_fams_dict['hard'])

print("Hard plant tissue targeting specific families:")
for fam in unique_hard_fams:
    print(fam)
print("Soft plant tissue targeting specific families:")
for fam in unique_soft_fams:
    print(fam)

Identifying Soft and Hard plant tissue targeting families:   0%|          | 0/711 [00:00<?, ?it/s]

Hard plant tissue targeting specific families:
GH77
PL22
GT20
GT28
CE1
CE11
GH31
GH37
Soft plant tissue targeting specific families:
CBM13
GH53
CBM63
GH94
GT97
GT19
AA3
GT1
PL4
CBM0
CBM32
GT30
GH5
PL9
GT56
GH103
GT41
GH30
GH102
GH2
GH38
GH18
PL26
GT32
GT84
PL2
CBM91

Compile all this data on genus and phenotype specific CAZy families into a single dataframe that will be similar to one presented in a paper/report.

# convert into df
unique_grp_data = []

unique_grp_fams['Hard tissue'] = unique_hard_fams
unique_grp_fams['Soft tissue'] = unique_soft_fams

for genus in unique_grp_fams:
    new_data = [genus]
    for cazy_class in ['GH', 'GT', 'CE', 'PL', 'AA', 'CBM']:
        added = False
        class_data = []
        for fam in unique_grp_fams[genus]:
            if fam.startswith(cazy_class):
                class_data.append(fam)
                added = True
        if added is False:
            class_data.append("")
        class_data.sort()
        new_data.append(", ".join(class_data))
            
    unique_grp_data.append(new_data)
    
unique_grp_df = pd.DataFrame(unique_grp_data, columns=['Genus', 'GH', 'GT', 'CE', 'PL', 'AA', 'CBM'])
unique_grp_df.to_csv("../results/cazy_families/unique_grp_fams.tsv", sep='\t')
unique_grp_df

	Genus	GH	GT	CE	PL	AA	CBM
0	Brenneria	GH106	GT21		PL17
1	Pectobacterium	GH121, GH146, GH18	GT101, GT102, GT11, GT111, GT14, GT24, GT52		PL11	AA10	CBM13
2	Dickeya	GH113, GH148, GH25, GH91	GT97	CE2	PL10		CBM4
3	Acerihabitans	GH127, GH15
4	Hard tissue	GH31, GH37, GH77	GT20, GT28	CE1, CE11	PL22
5	Soft tissue	GH102, GH103, GH18, GH2, GH30, GH38, GH5, GH53...	GT1, GT19, GT30, GT32, GT41, GT56, GT84, GT97		PL2, PL26, PL4, PL9	AA3	CBM0, CBM13, CBM32, CBM63, CBM91

Pectobacterium and Dickeya genus-specific families

Pectobacterium and Dickeya share a similar plant host range but show notable diversity in the compositions of the CAZyme-complements. drop all genomes not from Pectobacterium and Dickeya from the fam_freq_df dataframe, and repeat the analysis to identify genus specific CAZy families.

all_families = list(fam_freq_filtered_df.columns)[3:]
pd_fam_freq_df_filtered = fam_freq_filtered_df[fam_freq_filtered_df['Genus'].isin(
    ['Pectobacterium', 'Dickeya']
)]
# dict {group: {only unique fams}} and dict {group: {all fams}}
pd_unique_grp_fams, pd_group_fams = get_group_specific_fams(pd_fam_freq_df_filtered, 'Genus', all_families)
pd_unique_grp_fams

Identifying fams in each Genus: 100%|██████████| 632/632 [00:15<00:00, 40.46it/s]
Identifying Genus specific fams: 100%|██████████| 2/2 [00:00<00:00, 12000.87it/s]

{'Pectobacterium': {'AA10',
  'CBM13',
  'CBM18',
  'CBM3',
  'CBM67',
  'GH108',
  'GH12',
  'GH121',
  'GH146',
  'GH153',
  'GH154',
  'GH18',
  'GH38',
  'GH65',
  'GH68',
  'GT101',
  'GT102',
  'GT11',
  'GT111',
  'GT14',
  'GT20',
  'GT24',
  'GT32',
  'GT52',
  'GT73',
  'PL11'},
 'Dickeya': {'CBM4',
  'CE2',
  'GH113',
  'GH148',
  'GH25',
  'GH26',
  'GH91',
  'GT97',
  'PL0',
  'PL10'}}

4. The Core CAZome

Identify CAzy families that are present in every genome in the dataset using the identify_core_cazome() function from cazomevolve. These families form the 'core CAZome'.

The function takes one positional argument, a dataframe of CAZy family frequencies (with only CAZy families included in the columns, i.e no taxonomy columns).

Warning: The CAZy family frequency dataframe provided to <\b>identify_core_cazome()<\b> must contain no columns listing taxonomic data. Each column must represent a unique CAZy family.

# make output directory
# make output dir for results, will not delete data if dir already exists
make_output_directory(Path('../results/core_cazome/'), force=True, nodelete=True)

Built output directory: ../results/core_cazome

fam_freq_filtered_df_ggs = fam_freq_filtered_df.set_index(['Genome', 'Genus', 'Species'])

core_cazome = identify_core_cazome(fam_freq_filtered_df_ggs)

core_cazome = list(core_cazome)
core_cazome.sort()

print(f"Total families: {len(all_families)}")
print("The core CAZy families are:")
for fam in core_cazome:
    print('-', fam)

Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 7840.20it/s]

Total families: 117
The core CAZy families are:
- CBM5
- CBM50
- GH23
- GH3
- GT2
- GT51
- GT9

The boxplot shows the frequency of each CAZy family across all genomes in the dataframe. We can also break down this data by genus, and build a dataframe of Family, Genus (or tax rank of choice), genome, and frequency.

This dataframe can then be used to build a second dataframe of:

• Family
• Tax rank
• Mean frequency
• SD frequency Which can be presented as is in a report, or imported into RawGraphs to build a matrix plot (aka a proporitonal area plot).

# filter the famil freq df to include only those families in the core CAZome
core_cazome_df = fam_freq_filtered_df_ggs[core_cazome]
plot_fam_boxplot(core_cazome_df, font_scale=0.8, fig_size=(12,6))

The boxplot shows the frequency of each core CAZy family across all Pectobacteriaceae. To break down the frequency by genus, build a dataframe with the mean (and SD) of frequency of each family in the core CAZome per genus. This dataframe can then be used to plot a proportional area plot of the mean frequency of each CAZy family per genus, for exampling using RawGraphs.

core_cazome_df_genus = copy(core_cazome_df)  # to ensure core_cazome_df is not altereted
core_cazome_df_genus = add_tax_column_from_row_index(core_cazome_df_genus, 'Genus', 1)
core_cazome_df_genus.head()

			CBM5	CBM50	GH23	GH3	GT2	GT51	GT9	Genus
Genome	Genus	Species
GCA_005484945.1	Brenneria	rubrifaciens	1	6	6	2	8	3	3	Brenneria
GCA_003121725.1	Pectobacterium	atrosepticum	1	6	8	3	9	5	4	Pectobacterium
GCA_020406855.1	Pectobacterium	carotovorum	1	6	6	2	9	3	3	Pectobacterium
GCA_003121785.1	Pectobacterium	parmentieri	1	6	6	3	11	4	3	Pectobacterium
GCA_022748065.1	Dickeya	dianthicola	2	6	4	3	8	3	4	Dickeya

core_cazome_fggf_df, core_cazome_mean_freq_df = build_fam_mean_freq_df(
    core_cazome_df_genus,
    'Genus',
    round_by=2,
)

# add rows showing the means across all pectobacteriaceae
all_pecto_core_fam_data = []
for fam in core_cazome_df_genus.columns:
    try:
        mean_freq = np.mean(core_cazome_df_genus[fam]).round(2)
        sd_freq = np.std(core_cazome_df_genus[fam]).round(2)
        all_pecto_core_fam_data.append([fam, 'Pectobacteriaceae', mean_freq, sd_freq])
    except TypeError: # tax column
        continue
    
temp_df = pd.DataFrame(all_pecto_core_fam_data, columns=['Family','Genus','MeanFreq','SdFreq'])
core_cazome_mean_freq_df = pd.concat([core_cazome_mean_freq_df, temp_df])

core_cazome_mean_freq_df.to_csv("../results/core_cazome/core_cazome_freqs.csv")

core_cazome_mean_freq_df

Building [fam, grp, genome, freq] df: 100%|██████████| 711/711 [00:00<00:00, 4352.21it/s]
Building [Fam, grp, mean freq, sd freq] df: 100%|██████████| 8/8 [00:00<00:00, 116.25it/s]

	Family	Genus	MeanFreq	SdFreq
0	CBM5	Dickeya	1.99	0.12
1	CBM50	Dickeya	5.97	0.28
2	GH23	Dickeya	6.04	1.44
3	GH3	Dickeya	3.04	0.26
4	GT2	Dickeya	9.50	1.79
...	...	...	...	...
2	GH23	Pectobacteriaceae	6.49	1.55
3	GH3	Pectobacteriaceae	2.49	0.60
4	GT2	Pectobacteriaceae	8.15	2.10
5	GT51	Pectobacteriaceae	3.08	0.37
6	GT9	Pectobacteriaceae	3.70	0.56

63 rows × 4 columns

Genus specific core CAZomes

As well as looking at the core CAZome across all Pectobacteriaceae, we can identify the core CAZome of each genus. This is still done using the identify_core_cazome() function from cazomevolve, however, we filter the dataframe each time to retain only rows with data for the genera of interest.

These data (listing the genus specific core CAZomes) is used to aupsetplot to highlight the differences and similarities between the core CAZomes.

Note: Only generate that are represented by more than one genome are included in this analysis of genus specific core CAZomes. Otherwise, for genera with only one genome representative, all families in that one genome will be listed in the core CAZome.

genera_of_interest = ['Pectobacterium', 'Dickeya', 'Musicola', 'Brenneria', 'Lonsdalea']
all_families = fam_freq_filtered_df_ggs.columns

core_cazomes = {}  # {genus: {fams}}
for genus in genera_of_interest:
    filtered_df = fam_freq_filtered_df[fam_freq_filtered_df['Genus'] == genus]
    temp_core_cazome = identify_core_cazome(filtered_df[all_families])
    temp_core_cazome = list(temp_core_cazome)
    temp_core_cazome.sort()
    core_cazomes[genus] = {'fams': temp_core_cazome, 'freqs': {len(filtered_df)}}
    
core_cazomes

Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 10105.09it/s]
Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 13519.95it/s]
Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 16623.20it/s]
Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 16232.79it/s]
Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 16493.58it/s]

{'Pectobacterium': {'fams': ['CBM5',
   'CBM50',
   'GH1',
   'GH103',
   'GH23',
   'GH28',
   'GH3',
   'GH43',
   'GT2',
   'GT51',
   'GT9',
   'PL1',
   'PL2',
   'PL22',
   'PL3',
   'PL9'],
  'freqs': {426}},
 'Dickeya': {'fams': ['CBM48',
   'CBM5',
   'CBM50',
   'CE4',
   'CE8',
   'GH1',
   'GH103',
   'GH105',
   'GH13',
   'GH23',
   'GH28',
   'GH3',
   'GH33',
   'GH73',
   'GH77',
   'GH8',
   'GT1',
   'GT19',
   'GT2',
   'GT28',
   'GT35',
   'GT4',
   'GT5',
   'GT51',
   'GT9',
   'PL1',
   'PL9'],
  'freqs': {206}},
 'Musicola': {'fams': ['CBM48',
   'CBM5',
   'CBM50',
   'CE1',
   'CE11',
   'CE12',
   'CE4',
   'CE8',
   'CE9',
   'GH1',
   'GH102',
   'GH103',
   'GH104',
   'GH105',
   'GH13',
   'GH19',
   'GH2',
   'GH23',
   'GH28',
   'GH3',
   'GH30',
   'GH31',
   'GH32',
   'GH33',
   'GH38',
   'GH5',
   'GH73',
   'GH77',
   'GH8',
   'GT0',
   'GT1',
   'GT19',
   'GT2',
   'GT26',
   'GT28',
   'GT30',
   'GT35',
   'GT4',
   'GT5',
   'GT51',
   'GT56',
   'GT81',
   'GT83',
   'GT9',
   'PL1',
   'PL2',
   'PL22',
   'PL9'],
  'freqs': {4}},
 'Brenneria': {'fams': ['CBM5',
   'CBM50',
   'CE11',
   'CE12',
   'CE9',
   'GH1',
   'GH102',
   'GH103',
   'GH13',
   'GH23',
   'GH28',
   'GH3',
   'GH32',
   'GH4',
   'GH68',
   'GH73',
   'GH94',
   'GT0',
   'GT19',
   'GT2',
   'GT26',
   'GT28',
   'GT30',
   'GT35',
   'GT4',
   'GT5',
   'GT51',
   'GT56',
   'GT8',
   'GT81',
   'GT84',
   'GT9'],
  'freqs': {33}},
 'Lonsdalea': {'fams': ['CBM32',
   'CBM5',
   'CBM50',
   'CE11',
   'CE4',
   'GH19',
   'GH23',
   'GH3',
   'GH32',
   'GH37',
   'GH68',
   'GH77',
   'GH8',
   'GT19',
   'GT2',
   'GT20',
   'GT26',
   'GT28',
   'GT4',
   'GT51',
   'GT56',
   'GT9'],
  'freqs': {39}}}

core_cazome_upsetplot_membership = []
core_cazome_upsetplot_membership = add_to_upsetplot_membership(
    core_cazome_upsetplot_membership,
    core_cazomes,
)
len(core_cazome_upsetplot_membership)

708

core_cazome_upsetplot = build_upsetplot(
    core_cazome_upsetplot_membership,
    sort_by='input',
    file_path='../results/core_cazome/genera_core_cazome.svg',
)

Phenotype specific core CAZomes

Again by filtering the rows in the dataframe of CAZyme family frequencies, we can identify a core CAZome in any custom subset of genomes. This includes identifing the core CAZomes in soft and hard plant tissue targeting Pectobacteriaceae CAZomes.

soft_genera = ['Pectobacterium', 'Dickeya', 'Musicola']
hard_genera = ['Brenneria', 'Lonsdalea', 'Samsonia', 'Affinibrenneria', 'Acerihabitans']
grps = [[soft_genera, 'Soft tissue targeting'], [hard_genera, 'Hard tissue targeting']]

all_families = fam_freq_filtered_df_ggs.columns

soft_hard_core_cazomes = {}  # {grp: {fams}}
for grp in tqdm(grps):
    # gather all rows containing the genera of interest
    filtered_df = fam_freq_filtered_df[fam_freq_filtered_df['Genus'].isin(grp[0])]
    temp_core_cazome = identify_core_cazome(filtered_df[all_families])
    temp_core_cazome = list(temp_core_cazome)
    temp_core_cazome.sort()
    try:
        soft_hard_core_cazomes[grp[1]]
    except KeyError:
        soft_hard_core_cazomes[grp[1]] = {'fams': set(), 'freqs': [0]}

    soft_hard_core_cazomes[grp[1]]['fams'] = soft_hard_core_cazomes[grp[1]]['fams'].union(
        set(temp_core_cazome)
    )
    soft_hard_core_cazomes[grp[1]]['freqs'][0] += len(filtered_df)
    
soft_hard_core_cazomes

  0%|          | 0/2 [00:00<?, ?it/s]

Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 8325.00it/s]

Identifying core CAZome: 100%|██████████| 117/117 [00:00<00:00, 15449.36it/s]

{'Soft tissue targeting': {'fams': {'CBM5',
   'CBM50',
   'GH1',
   'GH103',
   'GH23',
   'GH28',
   'GH3',
   'GT2',
   'GT51',
   'GT9',
   'PL1',
   'PL9'},
  'freqs': [636]},
 'Hard tissue targeting': {'fams': {'CBM5',
   'CBM50',
   'CE11',
   'GH23',
   'GH3',
   'GT19',
   'GT2',
   'GT26',
   'GT28',
   'GT4',
   'GT51',
   'GT56',
   'GT9'},
  'freqs': [75]}}

soft_hard_core_cazomes.update(core_cazomes)

core_cazome_upsetplot_membership = []
core_cazome_upsetplot_membership = add_to_upsetplot_membership(
    core_cazome_upsetplot_membership,
    soft_hard_core_cazomes,
)
len(core_cazome_upsetplot_membership)

1419

core_cazome_upsetplot = build_upsetplot(
    core_cazome_upsetplot_membership,
    file_path='../results/core_cazome/genera_soft_hard_core_cazome.svg',
)

5. Families that always occur together

We can use cazomevolve to identify CAZy families that are always present in a genome together, although each group of always co-occurring CAZy families may not be present in every genomes.

Note: This approach is extremely stringent with its definition. It does not not tolerate one CAZy family ever appearing without the other family in the same genome.

# make output dir for results, will not delete data if dir already exists
make_output_directory(Path('../results/cooccurring_families/'), force=True, nodelete=True)

# reminder of the structure of the df
fam_freq_filtered_df.head(1)

Built output directory: ../results/cooccurring_families

	Genome	Genus	Species	AA10	AA3	CBM0	CBM13	CBM18	CBM3	CBM32	...	PL11	PL17	PL2	PL22	PL26	PL3	PL35	PL38	PL4	PL9
0	GCA_005484945.1	Brenneria	rubrifaciens	0	1	0	0	0	1	0	...	0	0	0	0	0	1	0	0	0	0

1 rows × 120 columns

Using a correlation matrix

There are two approaches are implemented by cazomevolve to idenitfy CAZy families that always co-occur together. The first generates a correlation matrix.

CAZy families that always appear together can be identified by generating a correlation matrix using the Python package pandas, CAZy families that are always present together will have a correlation matrix of 1.

This can be done using the identify_cooccurring_fams_corrM() function. CAZy families that are always present in the genome (i.e. the core CAZome), or are absent from all genomes will be calulcated to have a correlation score of nan.

Note: In order to plot the correlation matrix, the fill_value key word for identify_cooccurring_fams_corrM() can be used to replace all nan values with an interger.

identify_cooccurring_fams_corrM() returns:

• a set of tuples, one tuple per group of always co-occurring CAZy families
• a correlation matrix

all_families = list(fam_freq_filtered_df.columns[3:])

cooccurring_families, fam_corr_M_filled = identify_cooccurring_fams_corrM(
    fam_freq_filtered_df,
    all_families,
    core_cazome=[],
    corrM_path="../results/cooccurring_families/fam_corr_M_filled.csv",
    fill_value=2,
)

Building binary fam freq df: 100%|██████████| 117/117 [00:00<00:00, 1726.99it/s]
Delete absent families: 100%|██████████| 117/117 [00:00<00:00, 8219.30it/s]
Identifying always co-occurring families: 100%|██████████| 117/117 [00:00<00:00, 1879.45it/s]

cooccurring_families

{('CBM4', 'GH148'), ('GH121', 'GH146'), ('GH127', 'GH15'), ('GH94', 'GT84')}

Generate a clustermap of the correlation matrix.

sns.clustermap(
    fam_corr_M_filled,
    cmap=sns.cubehelix_palette(rot=0, dark=2, light=0, reverse=True, as_cmap=True),
)

/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/home/emmah/.conda/envs/pectobacteriaceae/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

<seaborn.matrix.ClusterGrid at 0x7f0f7c0b79a0>

An iterative approach to identify co-occurring families

The second method implemented by cazomevolve to identify CAZy families that always co-occur together is an iterative approach:

Iterate through the dataframe of CAZy family frequencies in Pectobacteriaceae (fam_freq_df_filtered) and identify the groups of always co-occurring CAZy families (i.e. those families that are always present together) and count the number of genomes in which the families are present together.

This is done using the cazomevolve function calc_cooccuring_fam_freqs(), which returns a dictionary of groups of co-occurring CAZy families. The function takes as input:

1. The dataframe of CAZy family frequencies (it can include taxonomy information in columns)
2. A list of the CAZy families to analyse
3. (Optional) whether to include or exclude the core CAZome from the list of always co-occurring CAZy families.

cooccurring_fams_dict = calc_cooccuring_fam_freqs(
    fam_freq_filtered_df,
    list(all_families),
    exclude_core_cazome=False,
)
cooccurring_fams_dict

Identifying pairs of co-occurring families: 100%|██████████| 117/117 [00:01<00:00, 65.58it/s]
Combining pairs of co-occurring families: 100%|██████████| 25/25 [00:00<00:00, 168581.35it/s]

{0: {'fams': {'CBM4', 'GH148'}, 'freqs': {8}},
 1: {'fams': {'CBM5', 'CBM50', 'GH23', 'GH3', 'GT2', 'GT51', 'GT9'},
  'freqs': {711}},
 2: {'fams': {'GH121', 'GH146'}, 'freqs': {1}},
 3: {'fams': {'GH127', 'GH15'}, 'freqs': {1}},
 4: {'fams': {'GH94', 'GT84'}, 'freqs': {309}}}

Genus specific groups of always co-occurring CAZy families

Similar to above when exploring core CAZomes, we can filiter the dataframe of CAZy family frequencies and run the analysis to identify always co-occurring CAZy families on custom subsets of genomes.

In this instance we reran the analysis for each genus in Pectobacteriaceae to identify genus specific groups of always co-occurring CAZy families.

Note: We limited the analysis to only those genera represented by more than one genome. Looking at genera where only one genome was analysed, all families in the genome will be listed as always co-occurring.

genera_cooccuring_fams = {}  # {genus: cooccurring_fams_dict}

for genus in tqdm(
    ['Pectobacterium', 'Dickeya', 'Musicola', 'Lonsdalea', 'Brenneria'],
    desc="Identifying genus specific co-occurring fams",
):
    genus_fam_freq_df = fam_freq_filtered_df[fam_freq_filtered_df['Genus'] == genus]
    genus_cooccurring_fams_dict = calc_cooccuring_fam_freqs(
        genus_fam_freq_df,
        list(all_families),
        exclude_core_cazome=False,
    )
    genera_cooccuring_fams[genus] = genus_cooccurring_fams_dict
genera_cooccuring_fams

Identifying genus specific co-occurring fams:   0%|          | 0/5 [00:00<?, ?it/s]

Identifying pairs of co-occurring families:   0%|          | 0/117 [00:00<?, ?it/s]
Identifying pairs of co-occurring families:   9%|▉         | 11/117 [00:00<00:01, 99.30it/s]
Identifying pairs of co-occurring families:  19%|█▉        | 22/117 [00:00<00:00, 103.55it/s]
Identifying pairs of co-occurring families:  31%|███       | 36/117 [00:00<00:00, 115.98it/s]
Identifying pairs of co-occurring families:  43%|████▎     | 50/117 [00:00<00:00, 121.90it/s]
Identifying pairs of co-occurring families:  54%|█████▍    | 63/117 [00:00<00:00, 122.99it/s]
Identifying pairs of co-occurring families:  65%|██████▍   | 76/117 [00:00<00:00, 117.79it/s]
Identifying pairs of co-occurring families:  75%|███████▌  | 88/117 [00:00<00:00, 114.01it/s]
Identifying pairs of co-occurring families:  85%|████████▌ | 100/117 [00:00<00:00, 108.98it/s]
Identifying pairs of co-occurring families: 100%|██████████| 117/117 [00:01<00:00, 113.30it/s]

Combining pairs of co-occurring families: 100%|██████████| 135/135 [00:00<00:00, 381557.30it/s]

Identifying pairs of co-occurring families:   0%|          | 0/117 [00:00<?, ?it/s]
Identifying pairs of co-occurring families:  18%|█▊        | 21/117 [00:00<00:00, 202.39it/s]
Identifying pairs of co-occurring families:  41%|████      | 48/117 [00:00<00:00, 237.53it/s]
Identifying pairs of co-occurring families:  62%|██████▏   | 72/117 [00:00<00:00, 224.22it/s]
Identifying pairs of co-occurring families: 100%|██████████| 117/117 [00:00<00:00, 224.57it/s][A

Combining pairs of co-occurring families: 100%|██████████| 361/361 [00:00<00:00, 508613.95it/s]

Identifying pairs of co-occurring families:   0%|          | 0/117 [00:00<?, ?it/s]
Identifying pairs of co-occurring families: 100%|██████████| 117/117 [00:00<00:00, 1062.60it/s]

Combining pairs of co-occurring families: 100%|██████████| 1130/1130 [00:00<00:00, 796030.15it/s]

Identifying pairs of co-occurring families:   0%|          | 0/117 [00:00<?, ?it/s]
Identifying pairs of co-occurring families: 100%|██████████| 117/117 [00:00<00:00, 908.82it/s][A

Combining pairs of co-occurring families: 100%|██████████| 255/255 [00:00<00:00, 578446.47it/s]

Identifying pairs of co-occurring families:   0%|          | 0/117 [00:00<?, ?it/s]
Identifying pairs of co-occurring families: 100%|██████████| 117/117 [00:00<00:00, 594.72it/s][A

Combining pairs of co-occurring families: 100%|██████████| 500/500 [00:00<00:00, 676282.49it/s]

{'Pectobacterium': {0: {'fams': {'CBM3', 'GH5'}, 'freqs': {425}},
  1: {'fams': {'CBM48', 'CE8', 'CE9', 'GH13'}, 'freqs': {425}},
  2: {'fams': {'CBM5',
    'CBM50',
    'GH1',
    'GH103',
    'GH23',
    'GH28',
    'GH3',
    'GH43',
    'GT2',
    'GT51',
    'GT9',
    'PL1',
    'PL2',
    'PL22',
    'PL3',
    'PL9'},
   'freqs': {426}},
  3: {'fams': {'CE11', 'GH102', 'GH32'}, 'freqs': {425}},
  4: {'fams': {'GH105', 'GT56'}, 'freqs': {425}},
  5: {'fams': {'GH121', 'GH146', 'GH154'}, 'freqs': {1}},
  6: {'fams': {'GH94', 'GT84'}, 'freqs': {152}}},
 'Dickeya': {0: {'fams': {'CBM4', 'GH148'}, 'freqs': {8}},
  1: {'fams': {'CBM48',
    'CBM5',
    'CBM50',
    'CE4',
    'CE8',
    'GH1',
    'GH103',
    'GH105',
    'GH13',
    'GH23',
    'GH28',
    'GH3',
    'GH33',
    'GH73',
    'GH77',
    'GH8',
    'GT1',
    'GT19',
    'GT2',
    'GT28',
    'GT35',
    'GT4',
    'GT5',
    'GT51',
    'GT9',
    'PL1',
    'PL9'},
   'freqs': {206}},
  2: {'fams': {'CE11', 'GT83'}, 'freqs': {204}},
  3: {'fams': {'GH16', 'GT25'}, 'freqs': {1}},
  4: {'fams': {'GH19', 'GH5', 'PL4'}, 'freqs': {203}},
  5: {'fams': {'GH88', 'PL35'}, 'freqs': {3}},
  6: {'fams': {'GH94', 'GT84'}, 'freqs': {89}},
  7: {'fams': {'GT30', 'PL3'}, 'freqs': {205}},
  8: {'fams': {'PL2', 'PL22'}, 'freqs': {205}}},
 'Musicola': {0: {'fams': {'CBM32', 'CBM63'}, 'freqs': {2}},
  1: {'fams': {'CBM48',
    'CBM5',
    'CBM50',
    'CE1',
    'CE11',
    'CE12',
    'CE4',
    'CE8',
    'CE9',
    'GH1',
    'GH102',
    'GH103',
    'GH104',
    'GH105',
    'GH13',
    'GH19',
    'GH2',
    'GH23',
    'GH28',
    'GH3',
    'GH30',
    'GH31',
    'GH32',
    'GH33',
    'GH38',
    'GH5',
    'GH73',
    'GH77',
    'GH8',
    'GT0',
    'GT1',
    'GT19',
    'GT2',
    'GT26',
    'GT28',
    'GT30',
    'GT35',
    'GT4',
    'GT5',
    'GT51',
    'GT56',
    'GT81',
    'GT83',
    'GT9',
    'PL1',
    'PL2',
    'PL22',
    'PL9'},
   'freqs': {4}},
  2: {'fams': {'GH24', 'GH36'}, 'freqs': {2}}},
 'Lonsdalea': {0: {'fams': {'CBM32',
    'CBM5',
    'CBM50',
    'CE11',
    'CE4',
    'GH19',
    'GH23',
    'GH3',
    'GH32',
    'GH37',
    'GH68',
    'GH77',
    'GH8',
    'GT19',
    'GT2',
    'GT20',
    'GT26',
    'GT28',
    'GT4',
    'GT51',
    'GT56',
    'GT9'},
   'freqs': {39}},
  1: {'fams': {'GH1', 'GH28', 'GH4', 'GH73', 'GT0'}, 'freqs': {38}},
  2: {'fams': {'GH13', 'GH39', 'GT30', 'PL1', 'PL3'}, 'freqs': {38}},
  3: {'fams': {'GH26', 'GH51'}, 'freqs': {9}},
  4: {'fams': {'GH31', 'GT81'}, 'freqs': {38}},
  5: {'fams': {'GH78', 'GT1'}, 'freqs': {10}},
  6: {'fams': {'GH94', 'GT84'}, 'freqs': {33}}},
 'Brenneria': {0: {'fams': {'CBM3', 'GH5'}, 'freqs': {25}},
  1: {'fams': {'CBM5',
    'CBM50',
    'CE11',
    'CE12',
    'CE9',
    'GH1',
    'GH102',
    'GH103',
    'GH13',
    'GH23',
    'GH28',
    'GH3',
    'GH32',
    'GH4',
    'GH68',
    'GH73',
    'GH94',
    'GT0',
    'GT19',
    'GT2',
    'GT26',
    'GT28',
    'GT30',
    'GT35',
    'GT4',
    'GT5',
    'GT51',
    'GT56',
    'GT8',
    'GT81',
    'GT84',
    'GT9'},
   'freqs': {33}},
  2: {'fams': {'GH106', 'PL38'}, 'freqs': {1}},
  3: {'fams': {'GH8', 'GT83'}, 'freqs': {15}},
  4: {'fams': {'GT73', 'PL17'}, 'freqs': {1}}}}

Phenotype specific groups of always co-occurring CAZy families

Identify families that always co-occurring in soft and hard plant tissue genera.

soft_genera = ['Pectobacterium', 'Dickeya', 'Musicola']
hard_genera = ['Brenneria', 'Lonsdalea', 'Samsonia', 'Affinibrenneria', 'Acerihabitans']
# hard_genera = ['Brenneria', 'Lonsdalea']
grps = [[soft_genera, 'Soft tissue targeting'], [hard_genera, 'Hard tissue targeting']]

for grp in tqdm(grps):
    # gather all rows containing the genera of interest
    grp_fam_freq_df = fam_freq_filtered_df[fam_freq_filtered_df['Genus'].isin(grp[0])]
    
    grp_cooccurring_fams_dict = calc_cooccuring_fam_freqs(
        grp_fam_freq_df,
        list(all_families),
        exclude_core_cazome=False,
    )
    genera_cooccuring_fams[grp[1]] = grp_cooccurring_fams_dict

  0%|          | 0/2 [00:00<?, ?it/s]

Identifying pairs of co-occurring families:   0%|          | 0/117 [00:00<?, ?it/s]
Identifying pairs of co-occurring families:   6%|▌         | 7/117 [00:00<00:01, 69.35it/s]
Identifying pairs of co-occurring families:  12%|█▏        | 14/117 [00:00<00:01, 68.24it/s]
Identifying pairs of co-occurring families:  19%|█▉        | 22/117 [00:00<00:01, 69.78it/s]
Identifying pairs of co-occurring families:  26%|██▌       | 30/117 [00:00<00:01, 72.97it/s]
Identifying pairs of co-occurring families:  35%|███▌      | 41/117 [00:00<00:00, 82.26it/s]
Identifying pairs of co-occurring families:  43%|████▎     | 50/117 [00:00<00:00, 76.98it/s]
Identifying pairs of co-occurring families:  51%|█████▏    | 60/117 [00:00<00:00, 81.08it/s]
Identifying pairs of co-occurring families:  59%|█████▉    | 69/117 [00:00<00:00, 80.75it/s]
Identifying pairs of co-occurring families:  67%|██████▋   | 78/117 [00:01<00:00, 78.03it/s]
Identifying pairs of co-occurring families:  74%|███████▎  | 86/117 [00:01<00:00, 78.51it/s]
Identifying pairs of co-occurring families:  80%|████████  | 94/117 [00:01<00:00, 76.67it/s]
Identifying pairs of co-occurring families:  87%|████████▋ | 102/117 [00:01<00:00, 74.73it/s]
Identifying pairs of co-occurring families: 100%|██████████| 117/117 [00:01<00:00, 75.87it/s]

Combining pairs of co-occurring families: 100%|██████████| 75/75 [00:00<00:00, 317109.68it/s]

Identifying pairs of co-occurring families:   0%|          | 0/117 [00:00<?, ?it/s]
Identifying pairs of co-occurring families:  37%|███▋      | 43/117 [00:00<00:00, 425.49it/s]
Identifying pairs of co-occurring families: 100%|██████████| 117/117 [00:00<00:00, 398.74it/s][A

Combining pairs of co-occurring families: 100%|██████████| 91/91 [00:00<00:00, 388282.47it/s]

Build an upset plot of co-occurring CAZy families

One of the best ways to visualise the groups of always co-occurring CAZy families is to generate an upset plot.

Build an upsetplot (using the Python package upsetplot) to visulise the groups of always co-occurring CAZy families, additionally it will plot the number of genomes in which each group of co-occurring CAZy families were present.

First compile the data/membership for the upset plot by:

1. Creating an empty list to store the upset plot data
2. Adding to the empty list the data contained in each dictionary of co-occurring CAZy families by using the add_to_upsetplot_membership() function

upsetplot_membership = []
upsetplot_membership = add_to_upsetplot_membership(upsetplot_membership, cooccurring_fams_dict)

for genus in genera_cooccuring_fams:
    upsetplot_membership = add_to_upsetplot_membership(
        upsetplot_membership,
        genera_cooccuring_fams[genus],
    )

len(upsetplot_membership)

7233

Build the upset plot

Building the upset plot will include the core CAZomes across Pectobacteriaceae, per genus, and per all soft plant tissue targeting genera and all hard plant tissue targeting genera.

pectobact_upsetplot = build_upsetplot(
    upsetplot_membership,
    file_path='../results/cooccurring_families/pecto-cooccurring-families.svg',
)

Break down the incidences per genus

The upset plot generates a bar chart showing the number of genomes that each group of co-occuring CAZy families appeared in. However, this plots the total number across each of the groups (i.e. Pectobacterium, Dickeya, etc.).

To break down the indidence (i.e. the number of genomes that each group of co-occurring CAZy families were present in) per group, a dataframe listing each group of co-occurring CAZy families, the group (i.e. genus), and the respective frequency must be generated. This dataframe can then be used to generate a proportional area plot (or matrix plot), breaking down the incidence per group (i.e. genus).

The groups of co-occurring CAZy families must be listed in the same order as they are presented in the upset plot.

upset_plot_groups = get_upsetplot_grps(upsetplot_membership)

100%|██████████| 38/38 [00:01<00:00, 20.98it/s]

Compiling the data of the incidence of each grp of co-occurring CAZy families per group of interest (e.g. per genus), into a single dataframe.

Create an empty list to store all data for the dataframe, then use add_upsetplot_grp_freqs to add data of the incidence per group of co-occurring CAZy families to the list. build_upsetplot_matrix is then used to build the dataframe.

cooccurring_grp_freq_data = []  # empty list to store data for the df

# add pectobacteriaceae data
genera_cooccuring_fams['Pectobacteriaceae'] = cooccurring_fams_dict

# add data for each genus, all soft plant targeting and hard plant tissue targeting
cooccurring_grp_freq_data = add_upsetplot_grp_freqs(
    upset_plot_groups,
    cooccurring_grp_freq_data,
    genera_cooccuring_fams,
    genus,
    grp_sep=True,
    grp_order=[
        'Pectobacteriaceae', 
        'Pectobacterium', 'Dickeya', 'Musicola', 'Soft tissue targeting',
        'Brenneria', 'Lonsdalea', 'Hard tissue targeting',
    ],
    include_none=True,
)

Compiling co-occurring families incidence data: 100%|██████████| 38/38 [00:00<00:00, 32146.74it/s]

Build a single dataframe of co-occurring families, freq and group (e.g. genus).

But also list the information for each group in the same order the groups of CAZy families are listed in the upset plot. This allows a proportional area plot to be generated (for example, by using RawGraphs), which can then be combined with the upset plot (for example, using inkscape).

# build the dataframe
cooccurring_fams_freq_df = build_upsetplot_matrix(
    cooccurring_grp_freq_data,
    'Genus',
    file_path='../results/cooccurring_families/cooccurring_fams_freqs.csv',
)
cooccurring_fams_freq_df

	Families	Genus	Incidence
0	PL2+PL22	Pectobacteriaceae	NaN
1	PL2+PL22	Pectobacterium	NaN
2	PL2+PL22	Dickeya	205.0
3	PL2+PL22	Musicola	NaN
4	PL2+PL22	Soft tissue targeting	635.0
...	...	...	...
299	GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28...	Musicola	4.0
300	GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28...	Soft tissue targeting	NaN
301	GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28...	Brenneria	NaN
302	GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28...	Lonsdalea	NaN
303	GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28...	Hard tissue targeting	NaN

304 rows × 3 columns

Generate figure for paper

After analysing the data, mannually group of the soft and hard tissue targeting specific groups of CAZy families together and mannually define the order the groups are presented in the final upset plot (by setting the param sort_by to 'input').

grp_order = {
    'soft_grps': [  # grps only found in soft plant tissue targeting genomes
        'GH13+CBM48+CE8', # S
        'PL2+PL22', # # S D
        'GH148+CBM4', # S D
        'PL3+GT30', # D
        'PL35+GH88', # D
        'CE11+GT83', # D
        'GT25+GH16', # D
        'GH5+GH19+PL4', # D
        'GH121+GH146+GH154', # S P   
        'GH121+GH146',
        'GH105+GT56', # P
        'GH13+CBM48+CE8+CE9',  # P
        'CE11+GH32+GH102', # P
        
    ],
    'musicola': [  # grps found only in musicola
        'CBM32+CBM63',
        'GH24+GH36',
    ],
    'both_grps': [  # grps found in soft and hard plant tissue targeting genomes
        'GT84+GH94', # 
        'GH5+CBM3', #
    ],
    'hard_musicola_grps': [  # grps only found in hard plant tissue targeting genomes and Musicola
        'GH13+GT30',
        'GH1+GH73+GT0',
        'GT5+GT35+GT8',
        'GH15+GH127',
        'GT81+GH31', # L
        'GH8+GT83', # B
    ],
    'hard_grps': [ # grps only found in hard plant tissue targeting genomes
        'CBM67+GH65', # H
        'PL17+GT73', # L B
        'PL38+GH106', # L B
        'GT1+GH78', # L
        'GH26+GH51', # L
        'GH1+GH28+GH73+GT0+GH4',
        'GH13+PL1+PL3+GT30+GH39',
    ],
    'all_core_cazomes': [ # then core cazomes at the end
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9',  # pectobacteriaceae
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH28+PL1+GH103+PL9',  # soft plant tissue targeting
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28+PL1+GH103+PL9+CBM48+CE8+GH105+GT4+GT28+GT19+GH73+GT5+GT35+GH8+CE4+GH77+GT1+GH33',   # dickeya
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH28+PL1+GH103+PL9+PL2+PL22+PL3+GH43', # pectobacter 
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28+PL1+GH103+PL9+PL2+PL22+CBM48+CE8+CE11+GH5+GH105+GT56+GH32+GH102+CE9+GT4+GT28+GT19+GH73+GT30+GT5+GT35+GH8+CE4+GH77+GH19+GT83+GT1+GH33+GT26+GT0+GT81+GH31+CE12+GH38+GH30+CE1+GH2+GH104',  # musicola
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+CE11+GT56+GT4+GT28+GT19+GT26',  # hard
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+CE11+GT56+GH32+GT4+GT28+GT19+GH8+CE4+GH77+GH19+GT26+GH68+CBM32+GT20+GH37',   # lonsdalea
        'GH23+GH3+CBM5+CBM50+GT2+GT51+GT9+GH1+GH13+GH28+GH103+GT84+GH94+CE11+GT56+GH32+GH102+CE9+GT4+GT28+GT19+GH73+GT30+GT5+GT35+GT26+GT0+GT81+GH68+GH4+GT8+CE12', # bren
    ],
}
for grp in grp_order:
    grps = []
    for fams_str in grp_order[grp]:
        fams_list = fams_str.split("+")
        fams_list.sort()
        fams = "+".join(fams_list)
        grps.append(fams)
    grp_order[grp] = grps

paper_cooccurring_fams = {}  # {grp_num: {'fams': {fams}, 'freqs': {int}}}
num_of_grp = 0

for pheno_grp in grp_order:
    for fam_grp in grp_order[pheno_grp]:
        fams = fam_grp.split("+")
        fams.sort()
        
        for genus in ['Pectobacterium', 'Dickeya', 'Musicola', 'Soft tissue targeting', 'Hard tissue targeting', 'Brenneria', 'Lonsdalea']:
            # {grp num: {'fams': {fams}, 'freqs': {int}}}
            
            for grp_num in genera_cooccuring_fams[genus]:
                grp_fams = list(genera_cooccuring_fams[genus][grp_num]['fams'])
                grp_fams.sort()
                
                if grp_fams == fams:
                    
                    this_grp_num = None
                    
                    for co_grp_num in paper_cooccurring_fams:
                        if paper_cooccurring_fams[co_grp_num]['fams'] == genera_cooccuring_fams[genus][grp_num]['fams']:
                            this_grp_num = co_grp_num
                            
                    if this_grp_num is None:
                        this_grp_num = copy(num_of_grp)
                    
                        paper_cooccurring_fams[this_grp_num] = {
                            'fams': genera_cooccuring_fams[genus][grp_num]['fams'],
                            'freqs': genera_cooccuring_fams[genus][grp_num]['freqs']
                        }
                        
                        num_of_grp += 1

upsetplot_membership = []
upsetplot_membership = add_to_upsetplot_membership(upsetplot_membership, paper_cooccurring_fams)
len(upsetplot_membership)

5076

pectobact_upsetplot = build_upsetplot(
    upsetplot_membership,
    file_path='../results/cooccurring_families/paper-pecto-cooccurring-families.svg',
    sort_by='input',
)

Calculate the frequency of each group per genus to then build a matrix plot (or proportional area plot).

paper_cooccurring_freqs = []  # [fams, genus/grp, incidence/freq]
num_of_grp = 0

for grp_name in grp_order:
    for fams in grp_order[grp_name]:
        fams = fams.split("+")
        fams.sort()

        for genus in ['Soft tissue targeting', 'Pectobacterium', 'Dickeya', 'Musicola', 'Hard tissue targeting', 'Brenneria', 'Lonsdalea']:
            # {grp num: {'fams': {fams}, 'freqs': {int}}}

            for grp_num in genera_cooccuring_fams[genus]:
                grp_fams = list(genera_cooccuring_fams[genus][grp_num]['fams'])
                grp_fams.sort()

                if grp_fams == fams:
                    # found fams in genus

                    paper_cooccurring_freqs.append(
                        [
                            genera_cooccuring_fams[genus][grp_num]['fams'],
                            genus,
                            list(genera_cooccuring_fams[genus][grp_num]['freqs'])[0],
                        ]
                    )

# build the dataframe
cooccurring_fams_freq_df = build_upsetplot_matrix(
    paper_cooccurring_freqs,
    'Genus',
    file_path='../results/cooccurring_families/paper-cooccurring_fams_freqs.csv',
)
cooccurring_fams_freq_df

	Families	Genus	Incidence
0	{CBM48, CE8, GH13}	Soft tissue targeting	635
1	{PL22, PL2}	Soft tissue targeting	635
2	{PL22, PL2}	Dickeya	205
3	{CBM4, GH148}	Soft tissue targeting	8
4	{CBM4, GH148}	Dickeya	8
5	{GT30, PL3}	Dickeya	205
6	{PL35, GH88}	Dickeya	3
7	{CE11, GT83}	Dickeya	204
8	{GH16, GT25}	Dickeya	1
9	{GH5, PL4, GH19}	Dickeya	203
10	{GH121, GH146, GH154}	Soft tissue targeting	1
11	{GH121, GH146, GH154}	Pectobacterium	1
12	{GT56, GH105}	Pectobacterium	425
13	{CBM48, CE8, CE9, GH13}	Pectobacterium	425
14	{CE11, GH32, GH102}	Pectobacterium	425
15	{CBM63, CBM32}	Musicola	2
16	{GH24, GH36}	Musicola	2
17	{GT84, GH94}	Soft tissue targeting	241
18	{GT84, GH94}	Pectobacterium	152
19	{GT84, GH94}	Dickeya	89
20	{GT84, GH94}	Hard tissue targeting	68
21	{GT84, GH94}	Lonsdalea	33
22	{CBM3, GH5}	Pectobacterium	425
23	{CBM3, GH5}	Hard tissue targeting	25
24	{CBM3, GH5}	Brenneria	25
25	{GT30, GH13}	Hard tissue targeting	74
26	{GH1, GT0, GH73}	Hard tissue targeting	74
27	{GT8, GT5, GT35}	Hard tissue targeting	36
28	{GH127, GH15}	Hard tissue targeting	1
29	{GT81, GH31}	Lonsdalea	38
30	{GH8, GT83}	Brenneria	15
31	{CBM67, GH65}	Hard tissue targeting	1
32	{PL17, GT73}	Hard tissue targeting	1
33	{PL17, GT73}	Brenneria	1
34	{PL38, GH106}	Hard tissue targeting	1
35	{PL38, GH106}	Brenneria	1
36	{GT1, GH78}	Lonsdalea	10
37	{GH26, GH51}	Lonsdalea	9
38	{GH1, GH28, GT0, GH4, GH73}	Lonsdalea	38
39	{GH39, PL3, GT30, GH13, PL1}	Lonsdalea	38
40	{GH1, PL9, CBM5, GH103, GH28, GH3, GT2, GT51, ...	Soft tissue targeting	636
41	{CBM48, GH105, GH28, GT28, GT51, CBM50, GT9, G...	Dickeya	206
42	{GH1, PL22, PL9, CBM5, GH103, PL3, GH28, GH3, ...	Pectobacterium	426
43	{CBM48, GT26, GH105, GH28, GT28, GT81, GT51, C...	Musicola	4
44	{GT4, GT26, GT19, CBM5, CE11, GT56, GT28, GH3,...	Hard tissue targeting	75
45	{GT26, GT28, GT51, CBM50, GT9, GT19, CBM5, GH6...	Lonsdalea	39
46	{GT26, GH94, GH28, GT28, GT81, GT51, GH4, CBM5...	Brenneria	33

6. Principal Component Analysis (PCA)

Use principal component analysis to identify individual and groups of CAZy families that are strongly associated with divergence between the Pectobacteriaceae genera CAZomes in terms of CAZy family frequencies.

What is PCA?:

Principal component analysis (PCA) is a statistical method that transforms a high dimensional data set into a low dimensional data set, while retaining as much information as possible. This dimensional reduction is achieved through the generation of principal components (PCs). Each PC is a direction along which variation in the data set is maximal. The first PC (PC1) captures the greatest diversity in the data set, the second PC (PC2) the second greatest direversity, and so on. Each PC represents a group of variables in the original data set, with each variable contributing a different weighting to the PC. A single variable can be associated with more than one PC. The relationships between variables and PCs can be visualised by plotting the loadings (or weightings) for each varaible along each PC.

PCA

Use the cazomevolve function perform_pca() to perform a PCA on a dataframe where each row is a genome, and each column the frequency of a unique CAZy family - the columns in the dataframe must only contain numerical data (i.e. no taxonomic data).

# make output dir for results, will not delete data if dir already exists
make_output_directory(Path('../results/pca/'), force=True, nodelete=True)

Built output directory: ../results/pca

num_of_components = len(fam_freq_filtered_df_ggs.columns)
pectobact_pca, X_scaled = perform_pca(fam_freq_filtered_df_ggs, num_of_components)
pectobact_pca

PCA(n_components=117)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Explained cumulative variance

Explore the amount of variance in the dataset that is captured by the dimensional reduction performed by the PCA.

print(
    f"{round(pectobact_pca.explained_variance_ratio_.sum() * 100, 2)}% "
    "of the variance in the data set was catpured by the PCA"
)

cumExpVar = plot_explained_variance(
    pectobact_pca,
    num_of_components,
    file_path="../results/pca/pca_explained_variance.png",
)

100.0% of the variance in the data set was catpured by the PCA
Number of features needed to explain 0.95 fraction of total variance is 59. 

Variance captured per PC

Explore the variance in the data that is captured by each PC.

plot_scree(pectobact_pca, nComp=10, file_path="../results/pca/pectobact_pca_scree.png")

Explained variance for 1PC: 0.1578167077631129
Explained variance for 2PC: 0.11714531745192668
Explained variance for 3PC: 0.055353539233033246
Explained variance for 4PC: 0.04804711615760428
Explained variance for 5PC: 0.04031600741870648
Explained variance for 6PC: 0.029313990173379968
Explained variance for 7PC: 0.027663834661362074
Explained variance for 8PC: 0.02165359970544143
Explained variance for 9PC: 0.02118856319918562
Explained variance for 10PC: 0.019638464318788136

PC1 (15%) and PC2 (11%) capture a signficantly greater degree of the varaince in the data set than all other PCs.
PC3 (6%) and PC4 (5%) capture comparable degrees of the variance

Scatter and loadings plots

To explore the variance captured by each PC, plot different combinations of PCs onto a scatter plot where each axis represents a different PC and each point on the plot is a genome in the data set, using the plot_pca() function.

plot_pca() takes 6 positional argumets:

1. PCA object from peform_pca()
2. Scaling object (X_scaled) from perform_pca()
3. The dataframe of CAZy family frequencies, if you want to colour code the genomes by a specific grouping (i.e. by Genus), an additional column containing the grouping information needs to be added to the dataframe (e.g. listing the genus per genome)
4. The number of the first PC to be plotted, e.g. 1 for PC1 - int
5. The number of the second PC to be plotted, e.g. 2 for PC2 - int
6. The method to colour code the genomes by (e.g. 'Genus') - needs to match the name of the column containing the data in the dataframe of CAZy family frequencies

Owing to the majoirty of the variance captured by the PCA being captured by PCs 1-4, all possible combinations of these PCs were explored.

PCs 1 - 4

The PCs 1-4 capture more diversity in the data set than the other PCs, therefore, plot all combinations of these PCs against each other, projecting the genomes onto these PCs.

A pairplot is generated using Seaborn, plotting each potential pairs between PCs 1-4. A KDE plot (a special type of density or histogram plot) is generated on the diagonal.

First colour code and style each point (where each point represents a genome) by its genus classification.

fam_freq_filtered_df_ggs['Genus'] = list(fam_freq_filtered_df['Genus'])
fam_freq_filtered_df_ggs['Species'] = list(fam_freq_filtered_df['Species'])

X_pca = pectobact_pca.transform(X_scaled)

fam_freq_df_ggs_pc = copy(fam_freq_filtered_df_ggs)
colnames = []
for i in range(4):
    fam_freq_df_ggs_pc[f'PC{i+1} ({round(pectobact_pca.explained_variance_ratio_[i] * 100, 2)}%)'] = X_pca[:,i]
    colnames.append(f'PC{i+1} ({round(pectobact_pca.explained_variance_ratio_[i] * 100, 2)}%)')

g = sns.pairplot(
    fam_freq_df_ggs_pc,
    vars=colnames,
    hue="Genus",
    diag_kind="kde",
    markers=['o','X', '^', 'P', 'v', 'D', '<', 's'],
    height=3,
);

i = 0
for ax in g.axes.ravel():
    if ax is None:
        continue
    if i not in [0,5,10,15]:
        ax.axhline(0, linestyle='--', color='grey', linewidth=1.25);
        ax.axvline(0, linestyle='--', color='grey', linewidth=1.25);
    else:
        ax.axvline(0, linestyle='--', color='grey', linewidth=1.25);
    i += 1
    
plt.savefig(
    '../results/pca/pca_pc_screen_genus.svg',
    bbox_inches='tight',
    format='svg'
)

Colour code each point by its species classification.

g = sns.pairplot(
    fam_freq_df_ggs_pc,
    vars=colnames,
    hue="Species",
    diag_kind="kde",
    height=3,
);

i = 0
for ax in g.axes.ravel():
    if ax is None:
        continue
    if i not in [0,5,10,15]:
        ax.axhline(0, linestyle='--', color='grey', linewidth=1.25);
        ax.axvline(0, linestyle='--', color='grey', linewidth=1.25);
    else:
        ax.axvline(0, linestyle='--', color='grey', linewidth=1.25);
    i += 1
    
sns.move_legend(g, "lower center", bbox_to_anchor=(.5, 1), ncol=6, title='Species', frameon=False);
    
plt.savefig(
    '../results/pca/pca_pc_screen_species.svg',
    bbox_inches='tight',
    format='svg'
)

There are so many species that it is difficult to tell the colours apart. Therefore, pick out some stories of interest, colour code these genomes and leave the rest grey.

# pick out some stories and colour code the species plot accordingly

PC1 separates out the genomes in a manner that correlates with their genus classification: Pectobacterium genomes are locataed in the negative PC1 axis, and Dickeya genomes are located in the positive PC1 axis.

PCs 2-4 do not correlate with the genus classification. In plots (from PC1-PC4) show the genome clustering correlating with species classification.

Individual plots

Having made a pairwise plot. Generate a scatter and loadings plot for each pair of PCs from PC1-PC4. This will make it easier to look at the details in each plot, and will make it easier to tell species apart.

Additional pauses are placed into the code to allow time for the notebook to render to figure before generating the next. This does not impact the figures that are saved to disk, but if the pauses (time.sleep()) are excluded, data from one figure may appear in another.

PC1 vs PC2

pc_pair = (1,2)
output_dir = Path(f'../results/pca/PC{pc_pair[0]}-vs-PC{pc_pair[1]}')
make_output_directory(output_dir, force=True, nodelete=True)

for job in ['genus', 'species', 'loadings']:
    if job == 'genus':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Genera")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-genus.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Genus',
            style='Genus',
            file_path=out,
        );
        time.sleep(2)

    elif job == 'species':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Species")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-species.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Species',
            style='Species',
            file_path=out,
        );
        time.sleep(2)

    elif job == 'loadings':  # loadings plot
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - Loadings plot")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-loadings_plot.png'
        g = plot_loadings(
            pectobact_pca,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            threshold=0.3,
            fig_size=(10,10),
            file_path=out,
            font_size=11,
        );

Built output directory: ../results/pca/PC1-vs-PC2

PC1 vs PC2 - plotting Genera
Not applying hue order
Applying style
Not applying style order

PC1 vs PC2 - plotting Species
Not applying hue order
Applying style
Not applying style order

PC1 vs PC2 - Loadings plot

Regenerate the scatter plot of PC1 vs PC2, labelling the Dickeya genomes that are clustered with Musicola, and the Pectobacterium genomes that are on the PC1 +ve axis.

X_pca = pectobact_pca.transform(X_scaled)
plt.figure(figsize=(15,15))
sns.set(font_scale=1.15)
g = sns.scatterplot(
    x=X_pca[:,0],
    y=X_pca[:,1],
    data=fam_freq_filtered_df_ggs,
    hue='Genus',
    style='Genus',
    s=100,
    markers=True,
)

g.axhline(0, linestyle='--', color='grey', linewidth=1.25);
g.axvline(0, linestyle='--', color='grey', linewidth=1.25);

plt.ylabel(f"PC2 {100 * pectobact_pca.explained_variance_ratio_[1]:.2f}%");
plt.xlabel(f"PC1 {100 * pectobact_pca.explained_variance_ratio_[0]:.2f}%");
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0);

genome_lbls = ["-".join(_) for _ in fam_freq_df_ggs.index]
x_vals = X_pca[:,0]
y_vals = X_pca[:,1]

texts = [
    plt.text(
        xval,
        yval,
        lbl,
        ha='center',
        va='center',
        fontsize=12,
    ) for (xval, yval, lbl) in zip(
        x_vals, y_vals, genome_lbls
    ) if ((xval > 2) and (yval < 3.5) and (yval > 0) and (xval < 4)) or ((xval > 0.1) and (xval < 2.5) and (yval < 0))
]
adjustText.adjust_text(texts, arrowprops=dict(arrowstyle='-', color='black'));

plt.savefig('../results/pca/pca_pc1_vs_pc2_musicola_annotated.png', bbox_inches='tight', format='png')

PC1 vs PC3

pc_pair = (1,3)
output_dir = Path(f'../results/pca/PC{pc_pair[0]}-vs-PC{pc_pair[1]}')
make_output_directory(output_dir, force=True, nodelete=True)

for job in ['genus', 'species', 'loadings']:
    if job == 'genus':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Genera")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-genus.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Genus',
            style='Genus',
            file_path=out,
            figsize=(10,8),
        );
        time.sleep(2)

    elif job == 'species':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Species")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-species.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Species',
            style='Species',
            file_path=out,
            figsize=(10,8),
        );
        time.sleep(2)

    elif job == 'loadings':  # loadings plot
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - Loadings plot")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-loadings_plot.png'
        g = plot_loadings(
            pectobact_pca,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            threshold=0.3,
            fig_size=(10,10),
            file_path=out,
            font_size=11,
        );

Built output directory: ../results/pca/PC1-vs-PC3

PC1 vs PC3 - plotting Genera
Not applying hue order
Applying style
Not applying style order

PC1 vs PC3 - plotting Species
Not applying hue order
Applying style
Not applying style order

PC1 vs PC3 - Loadings plot

PC1 vs PC4

pc_pair = (1,4)
output_dir = Path(f'../results/pca/PC{pc_pair[0]}-vs-PC{pc_pair[1]}')
make_output_directory(output_dir, force=True, nodelete=True)

for job in ['genus', 'species', 'loadings']:
    if job == 'genus':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Genera")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-genus.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Genus',
            style='Genus',
            file_path=out,
            figsize=(10,8),
        );
        time.sleep(2)

    elif job == 'species':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Species")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-species.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Species',
            style='Species',
            file_path=out,
            figsize=(10,8),
        );
        time.sleep(2)

    elif job == 'loadings':  # loadings plot
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - Loadings plot")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-loadings_plot.png'
        g = plot_loadings(
            pectobact_pca,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            threshold=0.3,
            fig_size=(10,10),
            file_path=out,
            font_size=11,
        );

Built output directory: ../results/pca/PC1-vs-PC4

PC1 vs PC4 - plotting Genera
Not applying hue order
Applying style
Not applying style order

PC1 vs PC4 - plotting Species
Not applying hue order
Applying style
Not applying style order

PC1 vs PC4 - Loadings plot

PC2 vs PC3

pc_pair = (2,3)
output_dir = Path(f'../results/pca/PC{pc_pair[0]}-vs-PC{pc_pair[1]}')
make_output_directory(output_dir, force=True, nodelete=True)

for job in ['genus', 'species', 'loadings']:
    if job == 'genus':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Genera")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-genus.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Genus',
            style='Genus',
            file_path=out,
            figsize=(8,10),
        );
        time.sleep(2)

    elif job == 'species':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Species")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-species.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Species',
            style='Species',
            file_path=out,
            figsize=(8,10),
        );
        time.sleep(2)

    elif job == 'loadings':  # loadings plot
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - Loadings plot")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-loadings_plot.png'
        g = plot_loadings(
            pectobact_pca,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            threshold=0.3,
            fig_size=(10,10),
            file_path=out,
            font_size=11,
        );

Built output directory: ../results/pca/PC2-vs-PC3

PC2 vs PC3 - plotting Genera
Not applying hue order
Applying style
Not applying style order

PC2 vs PC3 - plotting Species
Not applying hue order
Applying style
Not applying style order

PC2 vs PC3 - Loadings plot

PC2 vs PC4

pc_pair = (2,4)
output_dir = Path(f'../results/pca/PC{pc_pair[0]}-vs-PC{pc_pair[1]}')
make_output_directory(output_dir, force=True, nodelete=True)

for job in ['genus', 'species', 'loadings']:
    if job == 'genus':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Genera")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-genus.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Genus',
            style='Genus',
            file_path=out,
            figsize=(10,10),
        );
        time.sleep(2)

    elif job == 'species':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Species")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-species.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Species',
            style='Species',
            file_path=out,
            figsize=(10,10),
        );
        time.sleep(2)

    elif job == 'loadings':  # loadings plot
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - Loadings plot")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-loadings_plot.png'
        g = plot_loadings(
            pectobact_pca,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            threshold=0.3,
            fig_size=(10,10),
            file_path=out,
            font_size=11,
        );

Built output directory: ../results/pca/PC2-vs-PC4

PC2 vs PC4 - plotting Genera
Not applying hue order
Applying style
Not applying style order

PC2 vs PC4 - plotting Species
Not applying hue order
Applying style
Not applying style order

PC2 vs PC4 - Loadings plot

PC3 vs PC4

pc_pair = (3,4)
output_dir = Path(f'../results/pca/PC{pc_pair[0]}-vs-PC{pc_pair[1]}')
make_output_directory(output_dir, force=True, nodelete=True)

for job in ['genus', 'species', 'loadings']:
    if job == 'genus':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Genera")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-genus.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Genus',
            style='Genus',
            file_path=out,
        );
        time.sleep(2)

    elif job == 'species':
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - plotting Species")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-species.png'
        g = plot_pca(
            pectobact_pca,
            X_scaled,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            'Species',
            style='Species',
            file_path=out,
        );
        time.sleep(2)

    elif job == 'loadings':  # loadings plot
        print(f"PC{pc_pair[0]} vs PC{pc_pair[1]} - Loadings plot")
        out = output_dir/f'pca_pc{pc_pair[0]}_vs_pc{pc_pair[1]}-loadings_plot.png'
        g = plot_loadings(
            pectobact_pca,
            fam_freq_filtered_df_ggs,
            pc_pair[0],
            pc_pair[1],
            threshold=0.3,
            fig_size=(10,10),
            file_path=out,
            font_size=11,
        );

Built output directory: ../results/pca/PC3-vs-PC4

PC3 vs PC4 - plotting Genera
Not applying hue order
Applying style
Not applying style order

PC3 vs PC4 - plotting Species
Not applying hue order
Applying style
Not applying style order

PC3 vs PC4 - Loadings plot

Generate the scatter plot of PC3 vs PC4 again with only Pectobacterium genomes to identify the species that are diverging from the centre of the plot.

X_pca = pectobact_pca.transform(X_scaled)

plt.figure(figsize=(10,7.5))
sns.set(font_scale=1.15)

temp_d_fam_freq_df_ggs = fam_freq_filtered_df_ggs[fam_freq_filtered_df_ggs['Genus'] == 'Pectobacterium']

g = sns.scatterplot(
    x=X_pca[:,2][fam_freq_filtered_df_ggs['Genus'] == 'Pectobacterium'],
    y=X_pca[:,3][fam_freq_filtered_df_ggs['Genus'] == 'Pectobacterium'],
    data=temp_d_fam_freq_df_ggs,
    hue='Species',
    style='Species',
    s=100,
    markers=True,
)

g.axhline(0, linestyle='--', color='grey', linewidth=1.25);
g.axvline(0, linestyle='--', color='grey', linewidth=1.25);

plt.ylabel(f"PC4 {100 * pectobact_pca.explained_variance_ratio_[3]:.2f}%");
plt.xlabel(f"PC3 {100 * pectobact_pca.explained_variance_ratio_[2]:.2f}%");

plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0);
sns.move_legend(g, "lower center", bbox_to_anchor=(.5, 1), ncol=4, title='Species', frameon=False);

plt.savefig(
    '../results/pca/p_pc3_pc4_pectobacterium_sp.svg',
    bbox_inches='tight',
    format='svg',
)